BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.geoinfo-lab.org// BEGIN:VTIMEZONE TZID:+07 BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:+07 TZOFFSETFROM:+0700 TZOFFSETTO:+0700 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-Z8VFSK@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T094500 DTEND;TZID=+07:20241216T101500 DESCRIPTION:Large Language Models (LLMs) have revolutionized numerous aspec ts of modern life\, demonstrating remarkable capabilities in language proc essing\, code generation\, and knowledge synthesis. Their potential extend s to supporting the achievement of various Sustainable Development Goals ( SDGs)\, offering innovative approaches to tackling complex global challeng es. One promising application area is the mapping and monitoring phenomena measurable through Earth Observation (EO) data. It's estimated that aroun d 40 of the 169 SDG targets and 30 of the 232 SDG indicators could benefit from the insights provided by the EO data analysis. The use of artificial intelligence (AI) for EO data analysis can further improve the number of SDG indicators that can be monitored with a higher accuracy and frequency. \nIn this context\, research is underway to develop multimodal LLMs capabl e of directly processing EO data. However\, these models are often computa tionally expensive to train\, develop\, and maintain\, making them less fe asible for low-capacity\, high-risk countries that urgently need technolog ical solutions for disaster mitigation. To address this challenge\, we int roduce SATGPT (accessible at satgpt.net)\, an innovative solution that lev erages the current capabilities of LLMs and integrates them with cloud com puting platforms and EO data. SATGPT represents a fully functional\, innov ative spatial decision support system designed for rapid deployment\, part icularly in resource-limited contexts.\nThis talk presents an instance of SATGPT configured for flood mapping\, as an example. It simplifies the pro cess with a user-friendly interface requiring only a prompt specifying flo od duration and location. SATGPT leverages LLMs to generate GEE code dynam ically\, access historical databases\, or perform unsupervised classificat ion to detect flooded areas. This innovative integration of LLMs with GEE enhances the speed\, accessibility\, and real-time capabilities of flood m apping\, making it more accessible to non-specialists and supporting resil ient disaster management practices.\nFurthermore\, to build the capacity t o use these technologies effectively\, the talk discusses the development of an online\, free\, and self-paced course titled "Introduction to Geospa tial Data Analysis with ChatGPT and Google Earth Engine." This course intr oduces participants to the fundamentals of ChatGPT and the Earth Engine Co de Editor platform\, empowering them to process and interpret geospatial d ata effectively outside the SATGPT. The innovative aspect of the course is developing the Geo-prompt engineering (GPE) concept\, which focuses on us ing spatial\, temporal\, and satellite sensor- specific information in the prompt engineering process. The course aims to foster broader adoption of SATGPT and similar tools\, equipping users with the knowledge and skills needed to leverage advanced technologies in disaster management. This talk is structured to provide a comprehensive overview of SATGPT and its contr ibution to enhancing flood mapping and disaster management in the Asia-Pac ific region. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:FOSS4G and Sustainable Development Goals in Asia and the Pacific - Hamid Mehmood URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/Z8VFSK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-STB3HV@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T103000 DTEND;TZID=+07:20241216T110000 DESCRIPTION:The global climate crisis poses significant challenges\, partic ularly for urban areas. Nebula\, a specialist in resilient and regenerativ e city development\, advocates for a paradigm shift towards creating "happ y future cities." Unlike traditional approaches that focus on rebuilding f rom scratch\, Nebula emphasizes fostering an “environmentally enhancing\ , restorative relationship” with nature. This explores how cities can pl ay a crucial role in addressing environmental challenges through strategic urban planning.\n\nWe pioneer a transformative approach to urban developm ent\, creating future cities that are not just smart\, but regenerative\, resilient\, and centered on the well-being of both people and the planet. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Resilient and Regenerative City Development in Response to the Glob al Climate Crisis - URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/STB3HV/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-MRVR7Q@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T110000 DTEND;TZID=+07:20241216T113000 DESCRIPTION:The research assessment is traditionally based on the evaluatio n of criteria such as the number of peer-reviewed publications\, impact fa ctor\, and number or amount of grant funding. Unfortunately\, this approa ch has been proved to deeply influenced the way of conducting research tha t focus on quantity rather then quality and not . To maximize the impact o f research as a practical mean to address societal challenges\, a new appr oach\, named Open Science\, has been endorsed worldwide by major funding a gencies over the last decade as the new research pathway. Quality and imp act\, collaboration and sharing\, diversity and equity\, transparency and efficiency has become the new paradigms to be pursued.\nTo foster the adop tion of the Open Science funding agencies are acting on two fronts: on one hand\, by influencing policies and requiring the adoption of open science practices as a condition for funding access (Open Access\, Open Data and Citizen Science)\, and on the other hand\, by focusing on incentives and e xploring new methods for evaluating scientific results.\nIt is clear that in the near future\, the current “publish or perish” aphorism is shift ing towards “open or perish” to describe the required work to succeed in an academic career. But how should a modern researcher act and comply w ith this new paradigm? It essential for her/him to understand the best pra ctices that guarantee the recognition of her/his achievements by connectin g the researcher\, the publications\, the software and the data. In this t alk\, an introduction of these best practices is addressed with the aim of sustain the Open Science adoption with particular reference to Open Softw are. Finally\, new possible approaches envisioned for the evaluation of pr oject proposals\, career advancements and institutions assessment are pres ented and discussed. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Open or Perish - Cannata Massimiliano URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/MRVR7Q/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-V97YML@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T113000 DTEND;TZID=+07:20241216T120000 DESCRIPTION:My presentation will explore the landscape of open data in Thai land\, focusing on laws\, regulations\, policies\, and the fundamental rig ht to access information. It will assess how open data initiatives can fos ter transparency\, innovation\, and public engagement\, while addressing c hallenges and proposing solutions. Special emphasis will be placed on how open data can play a crucial role in reducing inequality by empowering cit izens with greater access to information\, enabling more equitable partici pation in decision-making processes\, and driving inclusive social develop ment. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Thailand Dialogue on Open Data Governance\, Privacy\, and Legality - Assist.Prof.Prapaporn Rojsiriruch URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/V97YML/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-RQFWXY@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T143000 DTEND;TZID=+07:20241216T145000 DESCRIPTION:Over recent years\, Mysore\, a district in Karnataka\, India\, has seen remarkable urban growth and infrastructural development\, transfo rming its landscape significantly. This study examines how this urban expa nsion influences property values\, using data from 2014 and 2024 to foreca st property values for 2034 with a Random Forest regression model. We focu s on 110 key locations\, looking at factors such as closeness to the centr al business district\, railway station\, bus stand\, and local amenities l ike schools and hospitals. By finding the strongest correlations between t hese elements\, we establish a relationship between property values and th ese factors to predict future values.\n\nOur findings highlight Mysore's v ibrant economic growth and its potential for sustained progress. These ins ights are crucial for the real estate market\, providing valuable informat ion to make informed decisions about future property values amid ongoing u rban development. By analyzing how urban growth impacts property values th rough sophisticated statistical models\, this study sheds light on how inf rastructural improvements and strategic locations drive real estate trends . The expected significant rise in property values by 2034 underscores Mys ore's economic dynamism and its appeal as an emerging urban hub.\n\nWe con ducted a thorough analysis of various factors affecting property values\, focusing on proximity to essential services and transportation hubs. These elements significantly influence property desirability and accessibility. Our use of the Random Forest regression model enables accurate prediction s of future property values by understanding complex relationships between these variables. The strong correlation between guideline values and mark et values provides a reliable basis for predicting future real estate tren ds. This correlation is essential for stakeholders\, including developers\ , investors\, and policymakers\, as it supports strategic decision-making based on market projections.\n\nThe expected significant rise in property values indicates that Mysore is poised for considerable growth\, driven by strategic developments and improved infrastructure. By understanding thes e trends\, stakeholders can make informed decisions to capitalize on Mysor e’s ongoing urban expansion\, ensuring that investments and development strategies align with the city's projected economic vitality and growth po tential. Our analysis highlights that proximity to the central business di strict\, bus stops\, and railway stations are key determinants of property values\, greatly influencing market prices. We project a significant incr ease in property values\, estimating a 118% rise by 2034.\n\nTo visualize these future values\, we employ Voronoi polygons\, which offer a clear spa tial representation of the predicted property value distribution. This app roach provides stakeholders\, including developers\, investors\, and polic ymakers\, with valuable insights into future market trends. By understandi ng the impact of these location factors\, they can make informed decisions regarding investments and development strategies. The anticipated rise in property values underscores the ongoing urban development and economic gr owth in Mysore\, highlighting its potential as a thriving urban center.\n\ nIn summary\, this study provides an in-depth analysis of the relationship between urban growth and property values in Mysore. By employing advanced regression models and detailed location-based data\, we have developed a robust forecast for property values in 2034. Our findings indicate a signi ficant projected increase in property values\, highlighting Mysore's conti nuous development and potential for future growth. These insights are esse ntial for the real estate market\, offering valuable guidance for future i nvestments and development strategies in Mysore. The study emphasizes the impact of key factors such as proximity to the central business district\, bus stops\, and railway stations on property values. By understanding the se dynamics\, stakeholders\, including developers\, investors\, and policy makers\, can make informed decisions to navigate the evolving real estate landscape. The anticipated rise in property values underscores Mysore’s economic vitality and its promise as a thriving urban center\, driven by s trategic infrastructure development.\n\nKeywords: Mysore\, Urban growth\, Property values\, Regression model\, Infrastructure\, Stakeholders DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Mysuru 2034: An Integrated Geoinformatics Approach for Real Estate Valuation and Urban Growth - CHANDAN M C\, Shreyanka M\, Nikitha K\, Tejas hvi Swamy\, Pramath Rathithara HP\, Dr. Kul Vaibhav Sharma URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/RQFWXY/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-WCMYUM@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T143000 DTEND;TZID=+07:20241216T144500 DESCRIPTION:In this session\, I will introduce mago3DTiler (https://github. com/Gaia3D/mago-3d-tiler)\, an open-source OGC 3D Tiles creator that has g ained global popularity thanks to its robust features\, high performance\, and user-friendly interface. Initially unveiled at FOSS4G-Asia 2023 in Se oul\, mago3DTiler supports over ten different 3D data formats\, including 3DS\, OBJ\, FBX\, glTF\, Collada DAE\, BIM (IFC)\, LAS\, LAZ\, and SHP. On e of its standout features is on-the-fly Coordinate Reference System (CRS) conversion during the 3D Tiles creation process. Additionally\, it allows users to convert 2D data with height attributes into extruded 3D Tiles.\n \nDuring this session\, I will also demonstrate how to create a digital tw in using mago3DTiler in just a few minutes. This tool makes complex geospa tial tasks more manageable\, especially for users looking to integrate div erse data formats seamlessly into 3D projects. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:State of mago3DTiler\, an Open Source Based OGC 3D Tiles Creator - Sanghee Shin URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/WCMYUM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-QRECLA@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T143000 DTEND;TZID=+07:20241216T144500 DESCRIPTION:The Meteorological Assimilation from Galileo and Drones for Agr iculture (MAGDA) project\, funded by EUSPA in the framework of the Horizon Europe program\, aims to develop a comprehensive toolchain for atmosphere monitoring\, weather forecasting\, and advisory services related to sever e weather\, irrigation\, and crop monitoring. By integrating GNSS\, Copern icus Sentinel\, Meteodrone\, ground-based weather radar\, and in-situ weat her and soil observations into open source weather and hydrological models \, MAGDA seeks to provide valuable information to agricultural operators. Measured data\, model results\, and warnings/advisories are delivered to f armers through a dedicated dashboard or by interfacing with existing Farm Management Systems. The technical and methodological components developed within MAGDA will form the basis for services supporting agricultural oper ations.\nThe project is based on the concept that continuous monitoring\, combined with advanced prediction models\, is essential for effective reso urce management. As extreme weather events\, such as droughts and heatwave s\, become more frequent due to climate change\, farmers need to leverage technology to mitigate disasters\, conserve resources\, and enhance produc tivity. A system that can automatically collect and process measurements o f key parameters significantly reduces economic losses. When this data is presented clearly and usefully to end users\, it can significantly enhance agricultural efficiency.\nMAGDA unites seven partners from seven European countries (Austria\, France\, Italy\, Romania\, Spain\, The Netherlands\, and Switzerland) and is inherently interdisciplinary\, drawing on experti se from various sectors to develop a system tailored to agricultural meteo rological and hydrological forecasts.\nThe selected demonstrator areas in Italy (Cuneo)\, France (Burgundy)\, and Romania (Braila) target different crops/cultures and allow for the gathering of different user needs and fee dback through direct interactions with farmers. Deployment includes nine l ow-cost\, dual-frequency GNSS stations\, along with fifteen low-cost in-si tu sensor stations\, and three Meteobases to fly meteodrones.\nSevere weat her cases were identified to test the open source WRF meteorological model 's performance: in Italy\, the focus was on rainfall events\, while in Fra nce and Romania\, hail events were prioritized. Water balance simulations were conducted to support an operational irrigation advisory service\, usi ng the open source SPHY hydrological model across the pilot areas in Franc e\, Italy\, and Romania.\nAll data used in the MAGDA project are open for research applications\, and the GNSS processing software utilized in this project leverages the goGPS open source software. The MAGDA dashboard for result visualization uses Leaflet as a web mapping tool and OpenStreetMap data as a background layer. The results presented here are derived from th e currently ongoing MAGDA demonstrators\, showcasing the project's impact on weather forecasting and water management for agricultural operations. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:The MAGDA Project: Integration of GNSS\, Sentinel\, Meteodrone\, an d In-Situ Observations for Weather Warnings and Irrigation Advisories in A griculture - Eugenio Realini URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/QRECLA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-7A9ZYA@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T144500 DTEND;TZID=+07:20241216T150000 DESCRIPTION:This talk presents a research case of an open source implementa tion of a task management system based on 3D spatial information web servi ce to efficiently conduct and manage tasks in the field of environmental i mpact assessment (EIA)\, which combines very diverse specialties. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:EIA(Environmental Impact Assessment) combined with 3D opensource ge ospatial. - Hakjoon Kim\, Yooyeol Yim\, Taiyoung Kim URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/7A9ZYA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-FNGVGL@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T144500 DTEND;TZID=+07:20241216T150000 DESCRIPTION:Vector tiles are changing the way we create maps. Client-side r endering offers endless possibilities to the cartographer and has introduc ed new map design tools and techniques. Let’s explore an innovative appr oach to modern cartography based on simplicity and a comprehensive vector tiles schema. Take a visual tour of vector tiles cartography\, and learn h ow to possibly adapt the map design for an asian audience. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Vector tiles cartography for Asia - Nicolas Bozon URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/FNGVGL/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-QV8FJA@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T145000 DTEND;TZID=+07:20241216T151000 DESCRIPTION:Mapping land suitability is a critical approach for identifying appropriate land use for site selection and land-use planning. However\, climate change exacerbates water shortages and droughts\, significantly af fecting land suitability and resulting in decreased crop yields\, especial ly for sugarcane. While land suitability is typically evaluated based on m ultiple criteria such as soil properties\, topography\, climate\, and soci oeconomic factors\, it is essential to incorporate drought conditions into land suitability mapping to mitigate climate change influences on crop yi elds. Therefore\, this study aimed to map sugarcane land suitability using fuzzy AHP and multi-criteria evaluation approaches in the Northeast regio n of Thailand.\n\nThe study selected six significant criteria for sugarcan e land suitability mapping: the ETDI as an agricultural drought index\, sl ope\, soil texture\, distance from the river\, distance from the road\, an d distance from the sugar mill. The ETDI was assessed by calculating the d ifference between spatial Potential Evapotranspiration (PET) and actual Ev apotranspiration (AET). Spatial PET was analyzed using a PET estimation mo del based on integrated GNSS-derived Precipitable Water Vapor\, processed with goGPS open-source software\, along with the MODIS land surface temper ature product. Concurrently\, the spatial AET was derived from the SEBAL m odel\, utilizing GRASS GIS software.\n\nSubsequently\, land suitability fo r sugarcane cultivation was evaluated by integrating fuzzy AHP and multi-c riteria evaluation approaches. The results indicated that two primary fact ors affected sugarcane cultivation: the ETDI and distance from the river. The ETDI was the most significant factor\, with an average weight of 0.66\ , while the distance from the river had an average weight of 0.34. Other f actors\, including slope\, soil texture\, distance from the road\, and dis tance from the sugar mill\, did not influence land suitability. The spatia l distribution of these factors was consistent throughout the study area.\ n\nSuitable areas for sugarcane were predominantly found in the moderately suitable class (S2\; 49.6%)\, followed by marginally suitable (S3\; 36.0% ) and highly suitable (S1\; 11.2%). Actual sugarcane cultivation areas wer e mainly in the S3 class (49.0%)\, followed by S2 (43.2%) and S1 (6.7%). S 3 class areas were concentrated in Wang Sam Mo district\, Udon Thani provi nce (129 km²)\, with a sugarcane yield of approximately 60.6 tons/ha. S2 class areas\, primarily in Phu Khiao district\, Chaiyaphum province (178 k m²)\, yielded about 62.5 tons/ha\, while S1 class areas in Phimai distric t\, Nakhon Ratchasima province (30 km²)\, achieved a higher yield of 63.6 tons/ha.\n\nS2 class areas could potentially be enhanced through irrigati on systems and small ponds to mitigate drought risks. Limiting the distanc e from the river to within 2 km could increase sugarcane yields and promot e areas to the S1 class\, expanding S1 areas by 2.7 times and raising yiel ds by approximately 1.1 tons/ha (1.8% of S2 yield). Areas classified as S1 exhibit significant potential for sugarcane cultivation expansion due to their underutilization. A total area of 6\,519 km² within the S1 class wa s analyzed for suitability. Nakhon Ratchasima province has the greatest po tential (2\,272 km²\, 35%)\, followed by Khon Kaen (725 km²\, 11%)\, Cha iyaphum (592 km²\, 9%)\, Udon Thani (519 km²\, 8%)\, and Surin (441 km² \, 7%).\n\nEncouraging a shift from currently cultivated crops (rice\, cor n\, and cassava) to sugarcane in these potential areas is essential for op timal resource utilization. However\, farmers often continue rice cultivat ion due to its traditional significance and shorter growth period\, provid ing quicker income. Government policies should support participatory knowl edge transfer on sugarcane cultivation\, ensure price guarantees\, and fac ilitate access to credit. Additionally\, the high price of sugarcane could incentivize farmers to expand sugarcane cultivation to meet increasing do mestic and export demand. Further research on a larger scale\, covering th e entire country\, is necessary to enhance the accuracy of land suitabilit y maps in addressing challenges posed by global climate change. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Mapping land suitability for sugarcane crop with fuzzy AHP and mult i-criteria evaluation - Piyanan Pipatsitee URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/QV8FJA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-D3LAUM@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T145000 DTEND;TZID=+07:20241216T151000 DESCRIPTION:Global remotely sensed evapotranspiration (RS-ET) products are increasingly pivotal in enhancing the accuracy and scope of hydrological m odeling\, particularly in regions where traditional ground-based streamflo w data are sparse or non-existent. These products play a pivotal role in u nderstanding the dynamics of the climate-soil-vegetation system\, where ev apotranspiration constitutes a substantial portion of water loss following precipitation events. Their extensive spatial coverage and accessibility have significantly expanded the capability to predict hydrological dynamic s in ungauged basins\, offering insights that were previously inaccessible through in-situ observations alone.\n\nDespite their benefits\, RS-ET pro ducts are tempered by inherent uncertainties\, primarily stemming from bia ses that vary across datasets and geographical regions. These biases manif est as either overestimation or underestimation compared to ground truth m easurements\, posing challenges for the accurate calibration of hydrologic al models. Traditional approaches in hydrological modeling commonly utiliz e absolute ET values directly derived from RS-ET products for model calibr ation\, without accounting for potential biases. However\, the reliability of such direct calibrations is contingent upon the quality and accuracy o f the RS-ET data\, which remains uncertain in many cases.\n\nTo address th ese challenges\, this study shifts the focus from absolute ET values to ut ilizing spatial structural information embedded within RS-ET data\, partic ularly emphasizing spatial autocorrelation\, which refers to the tendency of ET values at nearby locations to exhibit similarities. Employing the lo cal Moran's I index\, a spatially weighted autocorrelation statistic that is insensitive to biases\, we capture the spatial structure of ET data acr oss sub-basins. Additionally\, a composite Kling-Gupta Efficiency (KGE) me tric\, integrating absolute ET values and spatial autocorrelation informat ion in a weighted manner\, is employed for calibrating hydrological models .\n\nThree calibration schemes are thus designed to analyze the effectiven ess of spatial autocorrelation in hydrological modeling: one focusing sole ly on absolute ET values\, another solely on spatial autocorrelation\, and a combined approach. Testing these schemes for hydrological modeling with four RS-ET products in the Meichuan basin—MOD16\, GLASS\, and SSEBop wi th large biases\, and PMLV2 with minimal bias—the study demonstrates var ying effectiveness across these schemes.\n\nFor RS-ET products with substa ntial biases\, hydrological modeling using spatial autocorrelation proved to be the optimal solution. It achieved a higher KGE and lower Percent Bia s (PBIAS) on simulated streamflow compared to using solely the absolute ET value or the combined approach. Conversely\, for RS-ET products with mini mal biases\, hydrological models calibrated using both the combined approa ch were considered the preferred solutions. This approach can result in a high KGE\, similar to that obtained from spatial autocorrelation informati on alone\, while maintaining a reasonable PBIAS. Therefore\, we recommend calibrating hydrological models using both absolute ET values and spatial autocorrelation information in regions where ground ET observations are av ailable. This approach enhances the robustness and reliability of hydrolog ical predictions\, mitigating the influence of biases inherent in RS-ET pr oducts.\n\nIn contrast\, in scenarios where the quality of RS-ET products is unknown\, we suggest calibrating using only spatial autocorrelation inf ormation\, thereby circumventing potential biases and improving model accu racy under such circumstances. Moreover\, methodologically\, the study con tributes by demonstrating the efficacy of the local Moran's I index in cap turing the spatial structure of ET data within hydrological sub-basins. Th is geostatistical measure not only quantifies spatial autocorrelation but also identifies clusters and patterns of ET values\, thereby enriching our understanding of spatial variability in hydrological processes.\n\nFurthe rmore\, the comprehensive analysis of the composite KGE index underscores the significant contribution of spatial autocorrelation information to hyd rological modeling\, surpassing the influence of absolute ET values in enh ancing model performance. In conclusion\, the spatial autocorrelation-base d approach presented in this study represents a significant advancement in the application of global RS-ET products for hydrological modeling. By le veraging spatial structural information and mitigating biases inherent in RS-ET data\, this approach not only improves the accuracy of hydrological predictions but also enhances the practical utility of RS-ET products in d iverse hydrological contexts. Future research directions may explore addit ional spatial statistical techniques and incorporate a broader array of RS -ET datasets to further refine and validate these findings across differen t geographical settings and hydrological conditions. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Leveraging spatial autocorrelation information of remotely sensed e vapotranspiration for mitigating the impact of data uncertainty on hydrolo gical modeling - Yan He URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/D3LAUM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-39E8PC@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T150000 DTEND;TZID=+07:20241216T151500 DESCRIPTION:The range of AI applications in the geospatial information fiel d is diverse\, object detection\, area extraction\, change detection\, and super-resolution. In our industry\, we collectively refer to these techno logies as GeoAI.\n\nHowever\, these technologies remain primarily confined to specialized groups\, making it difficult to consider them as universal technologies ready for everyday use.\n\nIn light of this\, we sought to e xplore areas that could be easily accessible to the general public. Conseq uently\, we developed 'magoGPT'\, a application enabling users to manipula te maps and interact with 3D objects using natural language in a digital t win environment.\n\nBased on a 3D FOSS architecture composed of CesiumJS\, it uses 3DTiles buildings\, Terrain from high-resolution DEMs\, and multi ple layers visualisation. It is also related to artificial intelligence te chniques such as Large Language Models (LLM)\, Speech-to-Text (STT)\, and Natural Language Processing (NLP).\n\nIn this presentation\, we'd like to introduce magoGPT\, the technology behind it\, and the development process . DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Experience Digital Twin applications enhanced by AI prompts - Hanji n Lee\, Hyeeun Ahn\, Jaeseon Kim\, Heejin Ha\, Sanghee Shin URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/39E8PC/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-XEZMUZ@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T150000 DTEND;TZID=+07:20241216T151500 DESCRIPTION:In October 2018\, the art world witnessed an unprecedented even t when Banksy’s “Girl with Balloon” partially shredded itself immedi ately after being auctioned at Sotheby’s. This bold act challenged tradi tional perceptions of art\, value\, and the role of the artist. Drawing in spiration from this event\, I developed a unique QGIS plugin that shreds layers.\n\nThis plugin shreds the input data into shredded pieces and is i mplemented with a simple pyQGIS API.\nIt can be shedded with either vector or raster layers\, and the fineness of the shredding can be set.\n\n\nThi s plugin may seem useless at first glance\, but it can be used when you wa nt to mess up your data\, when you have created data that you cannot show to others\, when you just want to relieve stress\, or when you want to fee l like Banksy.\n\nMost importantly\, while shredding physical documents pr oduces waste\, shredding data creates no physical waste\, making it a trul y sustainable practice. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:The QGIS Shredder Plugin Inspired by Banksy’s Shredder: Sustainab le shredder with no waste - Naoya Nishibayashi URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/XEZMUZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-QPWNEZ@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T151000 DTEND;TZID=+07:20241216T153000 DESCRIPTION:Drought is a recurring issue in South Asia (SA) caused by extre me climate events\, posing ongoing challenges for food management\, sustai nable agricultural practices\, and livelihoods\, especially in frequently affected areas. Earth Observation (EO) data provides valuable information for long-term drought monitoring across wide regions. However\, there rema ins a need to map and monitor spatial drought events over large regions an d extended periods\, particularly in the Chi River Basin. In this region\, droughts have increasingly occurred with high frequency\, leading to low water-holding capacity and adversely affecting agricultural production and productivity.\n\nIn this context\, this study aimed: i) to identify spati al drought from 2001 to 2020 over the Chi River Basin\, Thailand using MOD IS image time series via Google Earth Engine (GEE)\; ii) analyse the corre lation between Temperature Vegetation Dryness Index (TVDI)\, Standardized Precipitation Index (SPI)\, and Streamflow Drought Index (SDI)\; and compa re severe drought areas to land use maps provided by the Land Development Department (LDD).\n\nIn this study\, we collected MODIS data using the MYD 09Q1 (250m spatial resolution) and MOD11A1 products (1000m pixel size) acr oss the h27v07 and h28v07 tiles. Image pre-processing was implemented\, co nsisting of image resampling\, data compositing\, and image mosaicking thr ough the GEE platform. Subsequently\, the TVDI index was generated for bot h dry and wet seasons to map and monitor the spatial distribution of droug ht events over 21 years. The spatial drought based on TVDI and meteorologi cal datasets (SPI and SDI) was determined to identify their relationship. Additionally\, this study compared the spatiotemporal distribution of drou ght to land use groups. \n\nHistorical droughts were most frequent during the dry seasons of 2005 (82%)\, 2013 (80%)\, and 2004 (78%)\, and appeared in the wet seasons of 2019 (41%)\, 2017 (41%)\, and 2009 (38%). The TVDI drought map had a slightly low coefficient of determination (R²) for SPI and SDI\, ranging from 0.12 to 0.22. However\, these findings showed simil ar trends of drought across all study years\, with drought events predomin antly occurring in the central and northeast parts of the region. In compa rison\, the spatial drought map in 2021 showed severe droughts\, mostly im pacting cassava and rice fields during the dry season and urban areas duri ng the wet season. Our proposed workflow is reliable and robust\, providin g spatial drought areas with confidence in the accuracy and validity of ou r results.\n\nThis study produced spatial drought maps using MODIS image t ime series datasets. The mapped results were well smoothed and effectively distributed drought areas across large regions. The highly severe spatial droughts in 2005 align with Thailand's extreme drought due to the El Niñ o event\, demonstrating high severity compared to other years. This confir ms that the TVDI index provides excellent and efficient map results for ma pping the spatial distribution of droughts in cloudy regions and complex l andscapes of the Chi River Basin. The proposed workflow can generate droug ht maps in cloudy regions and complex landscapes over large or national re gions\, particularly in zones like Thailand. Our findings can be used to m anage future droughts and serve as a significant tool for drought mitigati on planning and management\, as well as for warning systems\, providing an integrated model under climate change conditions.\n\n\n\nKeywords: Drough t\; earth observation\; Temperature Vegetation Dryness Index (TVDI)\; Land use\; Google Earth Engine DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Spatio-Temporal Drought Monitoring in the Chi River Basin from 2001 –2020 Using MODIS Time Series and Google Earth Engine - Jaturong Som-ard URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/QPWNEZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-WKWNW7@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T151000 DTEND;TZID=+07:20241216T153000 DESCRIPTION:This study aims to analyze the relationship between PM2.5 conce ntrations\, derived from Aerosol Optical Depth (AOD)\, and solar power gen eration a solar farm owned by A. Co.\, Ltd.(alias)\, in Samut Prakan Provi nce\, Thailand\, This research utilizes PM2.5 data from pollution monitori ng stations of the Pollution Control Department\, AOD data from the MCD19A 2.061 product\, and solar power generation data from the Electricity Gener ating Authority of Thailand in 2022. The results indicate in summer season a negative correlation between PM2.5 concentrations and solar power gener ation R² = -0.7\, meaning that as PM2.5 levels increase\, solar power gen eration decreases. A regression equation used for power prediction achieve d an accuracy of R² = 0.97. In contrast\, a positive correlation is obser ved during the winter season R² = 0.6\, indicating that as PM2.5 levels i ncrease\, solar power generation increases\, with a prediction accuracy of R² = 0.93. No significant correlation is found during the rainy season\, which may be due to other influencing factors. Predicting solar power gen eration in other areas should consider the different physical factors uniq ue to each location.\nKeywords: PM2.5\, Aerosol Optical Depth\, Solar Powe r\, Solar Farm DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:The relationship between PM2.5 and solar cell electricity generatio n Using Aerosol Optical Depth (AOD) - Sunattha Lalaeng URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/WKWNW7/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-NFCUBG@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T151500 DTEND;TZID=+07:20241216T153000 DESCRIPTION:In recent years\, the availability of geospatial data has signi ficantly increased\, with aerial photographs and Digital Elevation Models (DEMs) becoming widely accessible as open data. Additionally\, the acquisi tion of high-resolution image data through drones has become more feasible and commonplace. However\, extracting meaningful information from these v ast datasets remains a labor-intensive process\, often requiring significa nt time and resources.\n\nWhile various deep learning techniques have been employed to address this challenge\, they typically demand extensive effo rt in collecting and preparing training data. In light of these constraint s\, the Segment Anything Model (SAM) has emerged as a promising solution. SAM\, a recent development in the field of fundamental models\, offers the advantage of zero-shot classification without the need for specific train ing. Moreover\, its Apache-2.0 license ensures accessibility for a wide ra nge of applications.\n\nThis presentation aims to demonstrate the potentia l of SAM in the realm of geospatial information processing. We will showca se practical applications of SAM in analyzing geospatial data\, with a foc us on two critical areas: landslide detection and forest canopy mapping. T hese case studies will illustrate how SAM can efficiently process and extr act valuable insights from complex geospatial datasets\, potentially enhan cing the efficiency and effectiveness of our approach to environmental mon itoring and disaster risk assessment. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Exploring Segment Anything Model's Potential in Geospatial Data: Ca se Studies for Landslide and Forest Canopy Detection - Nobusuke Iwasaki\, Ayaka Onohara URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/NFCUBG/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-LXQ87Z@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T151500 DTEND;TZID=+07:20241216T153000 DESCRIPTION: DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Rust for Geospatial Data Processing: A Case Study with CityGML Conv erter for PLATEAU\, Japan's Open Digital Twin Models - Sorami Hisamoto URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/LXQ87Z/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-KQBSJ9@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T153000 DTEND;TZID=+07:20241216T154500 DESCRIPTION:Automating land use surveys in rural areas using advanced AI te chniques can significantly enhance the efficiency and accuracy of identify ing various land features. This project focuses on utilizing the YOLOv8 fr amework for land use detection through image segmentation and object detec tion.\nTraditional land use surveys in rural areas are time-consuming and often prone to inaccuracies due to manual methods. Leveraging artificial i ntelligence\, particularly deep learning models\, presents a promising sol ution to streamline this process and improve data reliability. The project addresses the challenge of automating land feature identification\, which includes detecting houses\, rivers\, roads\, and vegetation from visual d ata captured by satellite/drone. Accurate identification of these features is crucial for effective rural planning and development\, as it helps in resource allocation and infrastructure development. The limitations of con ventional methods\, such as the need for extensive human labor and suscept ibility to human error\, further highlight the necessity for innovative so lutions like AI-driven land use surveys.\nThe primary aim of this study is to develop and train an AI model capable of accurately detecting and segm enting various land features in rural landscapes. By doing so\, the projec t seeks to demonstrate the applicability of AI in enhancing rural developm ent planning and management. The specific objectives include creating a re liable dataset of annotated aerial images\, optimizing a deep learning mod el for high accuracy\, and evaluating the model's performance across diffe rent types of land features. Ultimately\, the project aims to provide a sc alable and efficient tool that can assist policymakers\, researchers\, and rural development planners in making informed decisions.\nThe methodology involved several key steps to ensure the robustness and accuracy of the A I model. First\, a diverse dataset of aerial images was collected\, encomp assing various rural landscapes with distinct features such as houses\, ri vers\, roads\, and farms/vegetation. These images were meticulously annota ted using specialized tools to create ground truth data for training and v alidation. Data augmentation techniques\, including rotations\, flips\, an d color adjustments\, were employed to expand the dataset and improve the model's generalization capabilities.\nThe YOLOv8 model was selected for it s state-of-the-art performance in object detection and segmentation tasks. YOLOv8's architecture is well-suited for real-time applications due to it s balance between accuracy and speed. The model was trained using the anno tated dataset\, with hyperparameters optimized to enhance its detection an d segmentation performance. Training was conducted on a high-performance c omputing setup\, leveraging GPU acceleration to expedite the process.\nThe results demonstrated the model's high precision in detecting and segmenti ng land features. The YOLOv8 model achieved notable accuracy metrics acros s various classes. The segmentation masks generated by the model closely m atched the ground truth annotations\, indicating its effectiveness in dist inguishing different land features.\nThe findings of this study underscore the potential of AI in transforming rural development practices. The succ essful application of the YOLOv8 model for land use detection highlights i ts capability to deliver precise and actionable insights. The practical im plications of this project are significant\, offering a scalable solution for land survey automation\, which can greatly assist policymakers and rur al planners. The integration of such AI-driven methodologies can lead to m ore informed decision-making\, efficient resource allocation\, and ultimat ely\, the betterment of rural communities.\nThe study also highlights seve ral challenges and limitations encountered during the project. Data collec tion in rural areas can be logistically challenging\, often requiring coll aboration with local authorities and stakeholders. Ensuring the diversity and quality of the dataset is crucial\, as biased or insufficient data can affect the model's performance. Additionally\, the model's accuracy is de pendent on the quality of annotations\, which requires meticulous effort a nd expertise.\nDespite these challenges\, the project demonstrates that AI can significantly enhance the accuracy and efficiency of land use surveys . The use of deep learning models like YOLOv8 can reduce the reliance on m anual methods\, providing a more reliable and scalable solution. However\, continuous efforts are needed to improve the dataset\, address potential biases\, and refine the model to handle more complex scenarios.\nIn conclu sion\, this project not only advances the field of AI in rural development but also sets a precedent for future studies aiming to leverage AI for si milar applications. The integration of AI in land use surveys can revoluti onize the way rural areas are planned and developed\, leading to more sust ainable and efficient outcomes. The success of this project inspires furth er research and development in AI-driven solutions for rural development\, with the potential to make a lasting positive impact on rural communities worldwide. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Land Use Detection Using Artificial Intelligence - Amritesh Hiras\, Anuj Sharad Mankumare\, Akshith Mynampati\, D ARUNA PRIYA URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/KQBSJ9/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-Q8QZUZ@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T153000 DTEND;TZID=+07:20241216T155000 DESCRIPTION:This research aims to estimate PM2.5 concentrations from Aeroso l Optical Depth (AOD) and meteorological data and study the spatial distri bution patterns of PM2.5 in Saraburi Province. The estimation of PM2.5 lev els is conducted using AOD data combined with meteorological data through a Multiple Linear Regression (MLR) method. The estimated values are then u sed to analyze the distribution patterns of PM2.5. The study found that in 2018\, the average monthly PM2.5 concentration ranged from 0 to 74.1 μg/ m³\, with high-value clustering (hot spots) covering approximately 421.43 km²\, or 12.04% of the provincial area. In 2019\, the average monthly PM 2.5 concentration ranged from 0 to 41.4 μg/m³\, with hot spots covering approximately 509.29 km²\, or 14.55% of the provincial area. In 2020\, th e average monthly PM2.5 concentration ranged from 0 to 50.0 μg/m³\, with hot spots covering approximately 648.37 km²\, or 18.53% of the provincia l area. In 2021\, the average monthly PM2.5 concentration ranged from 0 to 55.3 μg/m³\, with hot spots covering approximately 562.93 km²\, or 16. 09% of the provincial area. In 2022\, the average monthly PM2.5 concentrat ion ranged from 0 to 57.3 μg/m³\, with hot spots covering approximately 615.97 km²\, or 18% of the provincial area. The most of high-value cluste rs were in the western part of the province\, where agricultural activitie s are prevalent\, contributing to higher PM2.5 levels. In contrast\, low-v alue clusters (cold spots) were primarily found in the eastern part of the province\, which is largely forested. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:The Application of Google Earth Engine for PM2.5 Estimation to Anal yze PM2.5 Distribution From in Saraburi Province - Pattara URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/Q8QZUZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-J7DZR8@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T153000 DTEND;TZID=+07:20241216T154500 DESCRIPTION:In this session\, we will introduce Re:Earth Visualizer\, a 3D WebGIS platform\, covering its overview\, features\, technical components\ , and use cases. \nRe:Earth Visualizer is open-source tool that allows non -engineers to visualize data on maps without coding and publish it on the Web. It also includes a plugin system that enables users to develop and ad d custom functions like QGIS.\n\nWe will explain the system architecture\, use of advanced web technologies\, and integration with CesiumJS. Finally \, we will show several use cases demonstrating Re:Earth applications. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Introducing Re:Earth Visualizer - No-Code 3D WebGIS Powered by Cesi um - - Hinako Iseki URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/J7DZR8/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-YQSBBP@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T153000 DTEND;TZID=+07:20241216T155000 DESCRIPTION:According to the United Nations World Water Development Report\ , groundwater accounts for 26% of the world's renewable freshwater\, with around 2.5 billion people relying primarily on it for basic water needs. T he most realistic and cost-effective strategy to increase universal access to clean water\, meet the 2030 sustainable development goals (SDG)\, and minimize climate change impacts is broad exploitation and management of gr oundwater. The study area is Nigeria's capital Abuja\, generally character ized by moderate precipitation and few surface water sources. The water tr eatment plant\, designed with a capacity of 10\,000 cubic meters per hour of treated water\, was aimed at supporting a population of 500\,000 people 34 years ago. However\, due to population growth and urbanization\, the w ater supply is no longer meeting demand. Groundwater demand and consumptio n in Abuja have increased significantly over the last decade due to fast p opulation expansion\, urbanization\, and industrialization. Understanding groundwater potential and aquifer vulnerability is critical for sustainabl e resource management. \n\nGeologically\, Abuja is underlain by Precambria n rocks of the Nigerian Basement Complex\, which cover approximately 85% o f the land surface\, and sedimentary rocks\, which cover approximately 15% . In the study area\, four significant lithologic units are visible\; thes e include the Older Granites\, the Metasediments/Metavolcanics\, the Migma tite-Gneiss Complex\, and the Nupe sandstones of the Bida Basin\, which oc cupies the southwestern region of the territory.\n \nThis study aims to ma p groundwater potential and aquifer vulnerability zones using Hydrogeophys ical method\, which incorporates geoelectrical resistivity through vertica l electrical sounding (VES) and geographic information system (GIS) approa ches. With a maximum current electrode separation (AB/2) of 100m\, the Sch lumberger electrode configuration was used to acquire the field resistivit y data in 823 locations across the study area using a DC resistivity meter (Campus Ohmega Ω). \n\nThe resistivity method works by passing an elect ric current into the ground through two electrodes and measuring the conse quent potential difference across two other electrodes. The electrode spac ing gradually increases while the electrode array's center point remains f ixed. However\, as the current electrode spacing grows\, the current penet rates deeper into the ground\, and the apparent resistivity reflects the r esistivity of the deeper layers as well. The resistance is estimated as th e ratio of potential difference to current in ohms(Ω). Using a global po sitioning system (GPS)\, the absolute coordinates of the survey points (VE S) were determined. \n\nThree to five subsurface geoelectrical layers were identified in the research area with the aid of IPI2Win software. Vertica l electrical sounding (VES) data are often interpreted using the IPI2Win s oftware\, which is a user-friendly geophysical software designed to proces s resistivity data and generate one-dimensional models of subsurface layer s Layer resistivity and thickness were estimated using the software by ite rating the model with the observed field data acquired using the Schlumber ger array. The H-type sounding curve is the most dominant among the identi fied curve types. \n\nThe interpreted data were used to determine paramete rs including Depth to Bedrock\, Transverse Resistance\, Longitudinal Condu ctance\, Reflection Coefficient\, and Layer resistivity. Using scaling cri teria\, the longitudinal conductance was used to determine the aquifer pro tective Capacity (Vulnerability)\, and the result revealed the dominance o f moderate vulnerability across the study area.\n \nThe groundwater potent ial zones in the research area were characterized based on the following c riteria as established by previous authors in this field: Areas with overb urden thickness ≥ 30m and reflection coefficient < 0.8 were classified a s very high groundwater potential\; Areas with overburden thickness ≥13m and reflection coefficient < 0.8 were classified as High groundwater pote ntial\; Areas with overburden thickness ≥ 13m and reflection coefficient ≥ 0.8 were classified as moderate potential while areas with overburden thickness <13m and reflection coefficient ≥ 0.8 were classified as low potential\, and finally\, areas with overburden thickness >13m and reflect ion coefficient < 0.8. were classified as very low potential. These criter ia were written as Python codes that classify the area into five groundwat er potential zones. The area covered by each zone was calculated after the geospatial analysis: the very high GPZ occupies about 19.70% of the study area\, high 20.30%\, moderate 20.0%\, low 19.74%\, and very low 20.31%.\n \nOrdinary Kriging (OK) interpolation algorithm was used to generate the layer resistivity map\, layer thickness map\, depth to bedrock map\, aquif er vulnerability map\, and groundwater potential zone maps using the smart -map QGIS plugin. Smart-Map is a QGIS plugin that allows the generation of interpolated maps in the QGIS environment. Kriging is an unbiased linear interpolation technique that uses a weighted average of nearby samples to estimate unknown values in specific areas. It is deemed the best interpola tion method for spatially varying data. For this study\, the resistivity ( VES) data was randomly distributed over a large area\, and the sampling di stance between one VES data and the other ranged from 0.5km to 10km.\n\nTh is study evaluated groundwater parameters in the study area based on the g eo-electric properties of the earth material. The results reveal that weat hered/fractured basement and sandstone formations in the study area are su bstantial aquifer systems that host potable water. Data from some drilled boreholes across the study area were used to cross-validate the VES result s against borehole log records. This knowledge aided in a better understa nding of aquifer disposition\, vulnerability\, and potential consequences. The study's findings will provide a geo-database for groundwater potentia l zones in the Federal Capital Territory (Abuja)\, with significant implic ations for sustainable groundwater resource design and management. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Hydrogeophysical Analysis of Vertical Electrical Soundings for Grou ndwater Potential and Aquifer Vulnerability Evaluation in the Federal Capi tal Territory\, Abuja\, Nigeria - DANLAMI IBRAHIM URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/YQSBBP/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-TUJLSB@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T154500 DTEND;TZID=+07:20241216T160000 DESCRIPTION:“toeng.net”\, which we announced at FOSS4G-ASIA 2023 Seoul\ , will be launched as “MSpace.E”.\nMSpace.E is a comprehensive urban e nvironment analysis platform using 3D city models. This platform integrate s simulations of shadows\, building surface shadows\, noise\, and wind\, p roviding an approach to urban planning and environmental assessment.\n\nKe y features include 3D visualization using the Re:earth platform\, environm ental analysis integrated with user-provided construction data in IFC\, FB X\, GLB\, and 3D Tiles formats\, and newly added functionality for group m anagement and sharing of analysis results. Users can analyze the environme ntal factors within a selected area\, and by utilizing the PLATEAU 3D city model\, more accurate analysis is possible by taking into account existin g building structures. \n\nIn the case of shadow analysis\, users can sel ect analysis options\, specify the range\, set parameters\, and receive th e results in the CMZL file format. In addition\, users can upload their ow n construction data and visualize it in 3D on Re:earth. Group management a nd result sharing functions make team collaboration easier. We also discus s a comparison of the implementation and performance of MSpace.E with the toeng.net prototype published last year. Application areas include urban p lanning for optimal building placement and public space design\, environme ntal impact assessment of architectural projects\, and energy-saving strat egy planning at the urban scale.\n\nMSpace.E is a powerful platform for mu lti-faceted analysis and visualization of complex urban environments. It e nables comprehensive urban environment simulation\, strongly supporting de cision-making for sustainable urban development.\nJoin us at mspace.apptec .co.jp! DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:MSpace.E: Advanced Urban Environment Simulation Platform - NGUYEN V AN THIEN\, Hirofumi Hayashi\, iizukatoshiaki\, Hirosawa Kunihiko URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/TUJLSB/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-7AN83C@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T154500 DTEND;TZID=+07:20241216T160000 DESCRIPTION:Clouds\, atmospheric disturbances\, and sensor failures influen ce the quality of Earth observation (EO) data and satellite images\, in pa rticular. Many modeling techniques and statistical analysis\, to be applie d to EO data\, require the detection and removal of such aberrations. Howe ver\, the data gap created after removing the involved pixels needs to be imputed with numerical values that resemble the expected noncorrupted ones . Several imputation\, or gap-filling\, methods available in literature ar e based on time-series reconstruction\, working only on the temporal dimen sion of each pixel to input the missing values. Such methods\, compared to alternative ones that also consider spatial neighbor pixels or data fusio n with other sensors\, have the advantage of maintaining the same spatial resolution and spectral consistency in the imputed data.\nIn contrast with methods that only work with a local temporal window\, some of these metho ds take advantage of the whole time series of each pixel to reconstruct ea ch missing value\, allowing the full reconstruction of each gappy time-ser ies. Nevertheless\, such methods\, like most recent image propagation or l inear interpolation\, often require an iterative search of available value s along the time-series. When the time-series is composed of several sampl es and/or the involved number of pixels is large\, the application of such methods leads to prohibitive computational costs.\n\nWe present in this w ork a computational framework based on discrete convolution that numerical ly approximates such methods and does not require iterating over the time- series to be applied [1]. In addition\, the framework flexibility allows t he application of different time-series reconstruction methods by only ada pting the convolution kernel. The framework has been used to reconstruct t he PetaByte scale Landsat Analysis Ready Data (ARD) collection provided by the Global Land Analysis and Discovery team (GLAD) [2]. New research fron ts include the extension of the method to data-fusion approaches that comb ine time-series of multiple sensors to maintain the highest spatial resolu tion while also using the temporal information provided by all the sensors . The code\, developed in Python with a C++ backend to guarantee usability and high computational efficiency\, is openly available at https://github .com/openlandmap/scikit-map. \n\n\n\n[1] Consoli\, Davide & Parente\, Lean dro & Simoes\, Rolf & Murat\, & Tian\, Xuemeng & Witjes\, Martijn & Sloat\ , Lindsey & Hengl\, Tomislav. (2024). A computational framework for proces sing time-series of Earth Observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial reso lution. 10.21203/rs.3.rs-4465582/v1.\n\n[2] Potapov\, Peter & Hansen\, Mat thew & Kommareddy\, Anil & Kommareddy\, Anil & Turubanova\, Svetlana & Pic kens\, Amy & Adusei\, Bernard & Tyukavina\, Alexandra & Ying\, Qing. (2020 ). Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping. Remote Sensing. 12. 426. 10.3390/rs12030426. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Iteration-free methods for Earth observation data time-series recon struction - Davide Consoli URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/7AN83C/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-9EWY9A@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T161500 DTEND;TZID=+07:20241216T163000 DESCRIPTION:Disasters like typhoons\, earthquakes\, and flooding are inevit able\, especially as climate change intensifies. This heightens the need f or effective information sharing\, which some governments have addressed b y sending out timely digital alerts. While large organizations work to pre pare communities for disasters\, local knowledge often gets overlooked des pite its critical role in understanding a hazard’s impact. Residents pos sess intimate knowledge of their surroundings\, which becomes invaluable d uring emergencies for identifying evacuation routes and understanding the landscape. In creating a disaster risk assessment\, selecting data sources is crucial\, as demonstrated by the Metro Manila Climate and Disaster Ris k Atlas. This atlas\, created using QGIS and OSM\, assesses hazards in Met ro Manila—a region prone to a potential 7.2 magnitude earthquake due to the West Valley Fault System. Leveraging these tools\, provides a comprehe nsive view of risks\, empowering communities with vital information about the vulnerabilities and resilience of their locales. The project exemplifi es how integrating local and digital knowledge fosters a safer\, more prep ared society. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Empowering Citizen Scientists for Safer and Resilient Communities: A Story of Creating a Metro Manila Climate and Disaster Risk Atlas through QGIS and OSM - Janica Kylle De Guzman\, Ceejay T. Abilay URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/9EWY9A/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-EUZTM3@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T161500 DTEND;TZID=+07:20241216T163500 DESCRIPTION:Urban planning and mobility analysis have traditionally been st udied through observation or questionnaires\, which can be time-consuming and costly. However\, the rapid advancement of technology has enabled trac king devices to be installed in individual vehicles\, allowing the measure ment of various values\, particularly global positioning system (GPS) sign als.\n\nThe location data collected is accurate\, regularly updated\, and can offer valuable insights into people's movements and behavior. Because the amount of trajectory data is substantial and continues to increase ove r time\, specialized platforms and skills are needed for its analysis.\n\n In this study\, we developed large-scale analysis toolkits to extract insi ghts\, including trip statistics\, origin–destination analysis\, and hot spot identification from vehicle-based GPS trajectories. The toolkits are specifically designed to handle large-scale datasets using Apache Spark\, an analytics engine capable of processing large volumes of data by distrib uting tasks across a Hadoop cluster for efficient processing.\n\nAlgorithm s for the analytics model were created to reconstruct trips based on their type of mobility\, and trip locations were mapped using open data such as administrative boundaries and points of interest. We then verified our ap proach using real-world taxi data from Bangkok\, Thailand.\n\nThe results revealed that taxis had more vacant trips than busy trips\, and the travel time and distance taken to search for passengers were longer than those t aken to pick them up and drop them off. Taxi activity was concentrated in the city center and nearby areas\, particularly those within the vicinity of transport-connecting hubs. Taxi stay hotspots were mainly areas near to urist attractions and parking hubs.\n\nFurthermore\, we found that the pro cessing performance of the proposed approach increased with the number of executor cores. This study comprehensively presented information on taxi t ravel patterns\, service availability\, hotspots\, and processing performa nce using the developed trip analysis toolkits. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Development of Large-scale Trip Analysis Toolkits for Vehicle-based GPS Trajectories using Apache Spark and Open Data: A Case Study of Taxis in Bangkok\, Thailand - Apichon URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/EUZTM3/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-XYKJPP@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T161500 DTEND;TZID=+07:20241216T163000 DESCRIPTION:This session will explore how combining the powerful 3D mapping capabilities of Cesium with the intuitive and efficient web framework Sve lte can simplify the development of 3D map applications. We will demonstra te practical examples\, such as data binding and custom stores\, to show h ow these tools can make working with Cesium more straightforward and intui tive. Participants will gain new insights into using Svelte and Cesium tog ether\, making complex 3D geospatial projects more accessible and manageab le. This session is ideal for frontend engineers looking to enhance their development experience in the growing field of 3D mapping. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:From Complexity to Clarity: An Intuitive 3D Map Application Develop ment Experience with Cesium and Svelte - SUDA URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/XYKJPP/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-NQYUGR@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T161500 DTEND;TZID=+07:20241216T163000 DESCRIPTION:The integration of vast data analysis and AI technologies in ur ban planning represents a significant advancement in managing the complexi ties of modern cities. Through this presentation\, it show the vast potent ial of these technologies to enhance decision-making\, optimize resource a llocation\, and improve urban sustainability. By understanding and applyin g these technologies\, future urban planners can develop smarter\, more ef ficient\, and environmentally friendly cities. The practical applications discussed\, including traffic injury risk assessment\, human mobility anal ysis\, and carbon emission estimation\, demonstrate the tangible benefits of leveraging Big Earth Data and AI in urban planning. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Mapping Urban Dynamics: The Role of Data Analysis in Shaping Sustai nable Cities - Sarawut ninsawat URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/NQYUGR/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-XQWZE9@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T163000 DTEND;TZID=+07:20241216T164500 DESCRIPTION:Yen Bai Province in northern Vietnam\, especially Mu Cang Chai (MCC) and Van Yen (VY) districts\, are highly susceptible to slope failure due to rugged terrain\, high rainfall and anthropogenic activities . In t his research MCC was used as an area for training and testing the machine learning models\, while VY serves for model validation due to similar topo graphic and geological conditions. \n\nThe methodology treats the slope fa ilure prediction as a binary classification task (landslide/no-landslide). A balanced dataset of 286 landslide and 286 non-landslide points in MCC\, along with 16 contributing factors\, including topographic\, geologic\, h ydrologic\, anthropogenic and vegetation factors calculated from open data sources and made use from existing databases and from previous research o n the area. Principal Component Analysis (PCA) and Pearson Correlation Coe fficients refine the dataset by evaluating correlated factors and removing the least important ones\, the size of the training dataset can be reduce d while ensuring the performance of the ML models. Four ML models: Random Forest (RF)\, Support Vector Machine (SVM)\, Logistic Regression (LR)\, an d Extreme Gradient Boosting (XGBoost) are trained and evaluated to select the best hyperparameter tuning for each model. Model accuracy is assessed via confusion matrices\, accuracy score\, ROC (Receiver operating characte ristic) curves and AUC (Area under the ROC Curve). \n\nResults show the mo dels perform effectively in MCC with the average accuracy of all models be ing 0.74. The trained ML models with tuned hyper-parameters after running on MCC data\, was validated on datasets for VY. The VY data also consists of 16 factors\, with a data set of 308 landslide/non-landslide points. RF and XGBoost have the highest accuracy for both training and testing area ( MCC) and Validation area (VY)\, with XGBoost showing a slightly higher acc uracy score of 0.83 while RF scores 0.80.\n\nThe XGBoost model produces go od results and could be further optimized to achieve even better zonation in future studies. The machine learning workflow can be applied on other a reas that are prone to slope failures. Other geologic and weathering facto rs could be included in the analysis to further improve the model. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Comparitive Evaluation of Machine Learning Models for Zoning Slope Failure Suceptibility: A Case study of Yen Bai Province\, Vietnam - Tran T ung Lam\, Tatsuya Nemoto\, Kosuke Nakamura\, Sambuddha Dhar and Venkatesh Raghavan\, Bhuwan Awasthi\, Xuan Quang Truong URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/XQWZE9/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-UKEBWG@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T163000 DTEND;TZID=+07:20241216T164500 DESCRIPTION:As the maturity of digital twin technology increases over time\ , there is an increasing demand to implement various services (especially those related to decision support) that utilize invisible phenomena/analys is information by visualizing various types of sensor data or the results of professional analysis/prediction together\, from three-dimensional visu alization using only buildings/terrain.\n\nIn particular\, Korea aims to p rovide city-scale housing with a quality living environment\, and one of t he most complained about items is noise\, and there is a strong desire to utilize noise prediction analysis to derive planning/design results in urb an planning/design work.\n\nIn this presentation\, we will introduce a dig ital twin system that combines 3D spatial information and noise predictive modeling using "OGC standard CityGML" and "open source mago 3DTiler and m ago 3DTerrainer developed by Gaia3D" to support urban noise analysis and d ecision-making. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Implementation and visualization of a digital twin system for urban noise prediction - Haneul Yoo\, Yooyeol Yim\, Taiyoung Kim URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/UKEBWG/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-VGHKLP@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T163000 DTEND;TZID=+07:20241216T164500 DESCRIPTION:Effective data management is essential for maximizing the value of spatial data in today’s data-driven landscape. This presentation pro vides an overview of implementing ETL (Extract\, Transform\, Load) process es using NDJSON (Newline Delimited JSON) for efficient spatial data integr ation. We will discuss the importance of robust data management\, the bene fits of using NDJSON for handling large and complex spatial datasets\, and the practical applications of this approach.\n\nKey topics include the st eps of the ETL process with NDJSON\, from extracting spatial data from var ious sources\, transforming it into usable formats\, to loading it into da tabases such as MongoDB and Elasticsearch. We will highlight the efficienc y gains and flexibility provided by NDJSON in streaming and processing spa tial data. Additionally\, we will cover real-world use cases and best prac tices for optimizing spatial data integration with NDJSON.\n\nAttendees wi ll gain practical insights into the strategic and technical aspects of uti lizing NDJSON in ETL processes\, enabling them to implement effective spat ial data integration within their organizations. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Implementing ETL Processes with NDJSON for Spatial Data Integration - Athitaya Phankhan\, Chanakan Pangsapa URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/VGHKLP/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-3B3SDZ@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T163500 DTEND;TZID=+07:20241216T165500 DESCRIPTION:This research aims to develop a low-cost GNSS receiver device f or positioning agricultural tractors\, incorporating Differential GPS (DGP S) technology for enhanced accuracy using free and open source software. I ntegrated with IoT technology\, the device was tested to receive GNSS data and other relevant information\, including geographic coordinates (latitu de and longitude)\, tractor speed\, tractor direction\, date\, time\, and the number of satellites receiving signals. The DGPS setup involves using one receiver as a base station and another on the tractor\, where the base station provides correction data to improve positioning accuracy. The dat a collected by the receiver is transmitted to a signal processing device f or mapping the coordinates\, creating a route of the tractor's movement th at is displayed on a real-time Web Map Application. This process includes error correction to ensure high accuracy. The IoT device was installed on the left rear wheel of the agricultural tractor. Test results show that th e data from the developed device has an average accuracy of 22 centimeters \, which is acceptable and sufficient for agricultural tractor positioning applications. Furthermore\, this system enables real-time monitoring of t he tractor's operations. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Real-Time Monitoring and Positioning of Agricultural Tractors Using a Low-Cost GNSS and IoT Device - Thanwamas Phasinam URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/3B3SDZ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-8LLDDN@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T164500 DTEND;TZID=+07:20241216T170000 DESCRIPTION:This talk will explore the transition from proprietary nature t o PWAGIS QGIS plugins\, focusing on the limitations experienced with previ ous solutions of proprietary software\, the challenges encountered during development\, and the innovative functionalities introduced in the new plu gins. Attendees will gain insights into the practical aspects of moving fr om a legacy system to an open-source solution\, highlighting both the obst acles and the opportunities this transition presents. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:PWAGIS QGIS Plugins Development : Lessons learn to Free Open Source Solutions for Geospatial - Prasong Patheepphoemphong\, Pongsakorn Udombua URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/8LLDDN/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-7XHBUA@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T164500 DTEND;TZID=+07:20241216T170000 DESCRIPTION:The integration of Internet of Things (IoT) technology with env ironmental monitoring systems has significantly enhanced the ability to co llect real-time data from diverse and remote locations. However\, the chal lenge lies in efficiently analyzing and interpreting this vast amount of d ata to make informed decisions. This paper explores the application of gen erative AI in IoT data analysis for environmental monitoring. Generative A I\, with their advanced natural language processing capabilities\, offer a novel approach to processing and understanding complex data patterns. By leveraging Generative AI\, it is possible to automate the identification o f critical environmental changes\, predict trends\, and provide actionable insights with unprecedented accuracy. This study demonstrates how Generat ive AI can enhance data analytics in environmental monitoring through case studies that highlight improvements in air quality assessment (e.g. PM 2. 5). The findings suggest that Generative AI not only streamline the data a nalysis process but also enhance the reliability and responsiveness of env ironmental monitoring systems. Consequently\, this research underscores th e potential of Generative AI to transform IoT-based environmental monitori ng\, promoting more proactive and effective environmental management pract ices. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:From Data to Insights: The Impact of Generative AI on IoT-Based Env ironmental Monitoring - Dongpo Deng URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/7XHBUA/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-3M9EKG@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T164500 DTEND;TZID=+07:20241216T170000 DESCRIPTION:Geospatial technologies are being widely used to address societ al needs like land use\, demographics\, and natural resource management. S patial data analysis play a crucial role in how we understand and interact with our environment. They involve using maps\, GPS\, and satellite image s to collect\, analyse\, and display data about the world. Teaching these skills to school children has become increasingly important.\n\nGeospatial techniques help students develop a better understanding of the world arou nd them. Maps\, for instance\, are not just tools for finding directions\ ; they tell stories about our environment\, culture\, and history. By lear ning to read and create maps\, children begin to see the connections betwe en different places and the events that shape them. Understanding these co ncepts early helps them develop a broader view of the world and how places are interconnected This kind of knowledge fosters a global perspective\, encouraging students to think beyond their immediate surroundings and cons ider how their actions can affect the world.\n\nAs part of its mission to build awareness of the importance of Earth Science in daily life\, the tea m at ‘The Centre for Education and Research in Geosciences (CERG)\, Indi a conducts various activities aimed at laymen and schoolchildren. These ev ents are conducted throughout the year. One such program is a workshop ti tled “Maps & Me”\, which is focused on giving a basic understanding t o school & college children on the geospatial world and open-source mappin g tools. In the ‘Maps & Me’ workshop we explore the basics of maps\, s atellite images and digital maps and how to navigate using these tools.\n\ nThe workshop is hands-on and interactive\, covering key map elements like latitude\, longitude\, and scale\, along with a session on using QGIS\, a popular open-source mapping software. Participants are introduced to the fundamentals of remote sensing\, satellite imagery\, photo recognition\, a nd digital mapping. After that\, they get to create their own maps using Q GIS.\n\nAt CERG we believe that such skills are important because they hel p students make sense of real-world issues\, like climate change\, urban p lanning\, and natural resource management. By learning how to read and int erpret maps\, for example\, young students can see how their local environ ment fits into the larger world. It also encourages them to think critical ly and solve problems creatively\, skills that are valuable in all areas o f life. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Catch them young! Geospatial Capacity building for School Children and Young adults - Natraj Vaddadi URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/3M9EKG/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-YZRW9Y@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T165500 DTEND;TZID=+07:20241216T171500 DESCRIPTION:Introduction\n\nWith the spread of IoT and the increasing resol ution of observation sensors\, the total amount of geospatial information data is increasing exponentially daily. On the other hand\, the increase i n resolution of display devices used to analyze and visualize these data i s reaching its limit due to various physical constraints. The maximum reso lution of commercially available display devices is 8K\; 4K or 5K is consi dered the upper limit for desktop use.\nUsing the OS's multi-display funct ion or a tiled display driver provided by the GPU manufacturer\, it is pos sible to create a display environment with an even larger area and higher resolution. However\, the middleware provided by the GPU manufacturer curr ently has a maximum resolution limitation of 16K [1]\, which is the maximu m resolution that can be achieved on a single PC.\nHowever\, even if these mechanisms are used to create an ultra-high-resolution display environmen t\, it is only possible to render data within the web browser's heap memor y limit in the case of WebGIS applications. For example\, the 3DWebGIS vie wer provided by the Tokyo Digital Twin Project [2] cannot render 3DTiles [ 3] building data for all 23 wards of Tokyo at once (textured building data is used for areas provided with texture).\nIn this paper\, we introduce C hOWDER\, a web-based tiled display driver that enables distributed renderi ng of 3DWebGIS content across multiple web browsers\, as a solution to the above problems and report the results of memory load balancing experiment s using ChOWDER for distributed rendering.\n\nProposal of a distributed re ndering method for 3DWebGIS\n\nOne possible solution to the above problems is to distribute the display of one WebGIS content across multiple PCs (m ultiple web browsers). This makes it possible to display a WebGIS at a res olution that exceeds the upper limit of a single PC (web browser) and dist ributes the memory load required to display the content across each PC (we b browser).\nThe scalable display system ChOWDER[4][5]\, jointly developed by RIKEN Center for Computational Science and Kyushu University\, is an o pen-source tiled display driver that can create an ultra-high-resolution p ixel space by arranging multiple displays that display a web browser in fu ll-screen mode in tiles. It also supports distributed rendering of 3DWebGI S.\nThis function uses iTowns[6]\, an open-source 3DWebGIS\, as middleware . iTowns uses Three.js as a WebGL rendering library\, and Three.js has an API that can offset the view frustum[7].\nThe view frustum must be split a ppropriately to split and display 3D content on multiple display devices. ChOWDER uses the view frustum offset API of Three.js to split a single iTo wns content into multiple view frustums\, enabling multiple web browsers t o split and render 3DWebGIS content [8].\nHowever\, at the time of the pre vious report [8]\, when iTowns executed a 3DTiles load command\, it loaded all the data without judging whether it was inside or outside the view fr ustum range\, so distributed rendering did not improve memory utilization efficiency. Since then\, the 3DTiles load process was improved in iTowns R elease 2.42.0\; in this paper\, we measured the amount of heap memory cons umed by each browser when iTowns content was distributed and rendered usin g ChOWDER on multiple web browsers and confirmed the memory load distribut ion achieved by this method.\n\nExperimental procedures and results\n\nThe experimental data used was the textured building data for Chiyoda\, Minat o\, and Chuo wards in Tokyo\, from the 3DTiles data distributed by the PLA TEAU project [9] of the Ministry of Land\, Infrastructure\, Transport and Tourism of Japan.\nThe experiment first displayed the 3DTiles building dat a for the above three wards in full screen on a single 4K resolution displ ay using iTowns on ChOWDER. The heap memory size of the web browser at thi s time was 268MB.\nNext\, the same content was displayed on a ChOWDER dist ributed display consisting of four 4K displays arranged in two horizontal and two vertical rows. Each display had a full-screen web browser. The hea p memory sizes of each web browser were 133MB\, 188MB\, 68.3MB\, and 37.7M B.\nFinally\, we conducted an experiment using nine 4K displays arranged i n three rows and three columns. The heap memory sizes of each web browser were 66.8MB\, 122MB\, 140MB\, 84.3MB\, 87.2MB\, 56.9MB\, 41.2MB\, 38.4MB\, and 33.6MB.\nFrom these experimental results\, it can be said that distri buted rendering of 3DWebGIS using ChOWDER achieves memory load balancing.\ nDuring distributed rendering\, the heap memory size of each web browser i s different because the amount of 3DTiles data contained in each responsib le drawing area is different. Also\, the total heap memory size of all bro wsers is larger than when rendering in a single browser because iTowns loa ds 3DTiles data that is wider than its view frustum\, and data loading in overlapping areas occurs during distributed rendering.\n\nFuture work and conclusion\n\nIn this experiment\, we measured the web browser's heap memo ry\, but did not measure GPU memory consumption. However\, because 3DWebGI S uses WebGL for rendering\, we believe that a more precise evaluation can be made by measuring GPU memory consumption as well.\nIn addition\, since distributing rendering across more web browsers is expected to further di stribute memory load\, we plan to conduct experiments by increasing the nu mber of distributed displays.\nIn this paper\, we have shown the limitatio ns of current 3DWebGIS when the data to be displayed increases\, and propo sed a distributed rendering method as a means to solve this problem\, and introduced the view frustum offset API of Three.js\, iTowns\, a 3DWebGIS t o which it can be applied\, and ChOWDER\, a web-based tiled display driver that incorporates them\, as a means to realize this method. Furthermore\, we have presented the results of an experiment that shows that memory loa d distribution is achieved by distributed rendering using these and demons trated that this method is one solution to the increase in data to be disp layed in 3DWebGIS.\n\nReferences\n\n[1] Limitations. About NVIDIA Mosaic. https://www.nvidia.com/content/Control-Panel-Help/vLatest/en-us/mergedProj ects/nvwks/SLI_Mosaic_Mode.htm Accessed July 29\, 2024.\n\n[2] Tokyo Digit al Twin Project. https://info.tokyo-digitaltwin.metro.tokyo.lg.jp/ Accesse d July 29\, 2024.\n\n[3] 3DTiles. The open specification for 3D data. http s://cesium.com/why-cesium/3d-tiles/ Accessed July 29\, 2024.\n\n[4] Kawana be\, T.\, Nonaka\, J.\, Hatta\, K.\, & Ono\, K. (2018\, September). ChOWDE R: an adaptive tiled display wall driver for dynamic remote collaboration. In International Conference on Cooperative Design\, Visualization and Eng ineering (pp. 11-15). Cham: Springer International Publishing.\n\n[5] ChOW DER GitHub repository. https://github.com/SIPupstreamDesign/ChOWDER Access ed July 29\, 2024.\n\n[6] iTowns (in French). https://www.itowns-project.o rg/ Accessed July 29\, 2024.\n\n[7] three.js API Reference. https://threej s.org/docs/#api/en/cameras/PerspectiveCamera.setViewOffset Accessed July 2 9\, 2024.\n\n[8] Kawanabe\, T.\, Hatta\, K.\, & Ono\, K. (2020\, September ). ChOWDER: A New Approach for Viewing 3D Web GIS on Ultra-High-Resolution Scalable Display. In 2020 IEEE International Conference on Cluster Comput ing (CLUSTER) (pp. 412-413). IEEE.\n\n[9] Project PLATEAU portal site (in Japanese). https://www.geospatial.jp/ckan/dataset/plateau Accessed July 29 \, 2024. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:On the performance of distributed rendering system for 3DWebGIS app lication on ultra-high-resolution display - Tomohiro KAWANABE URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/YZRW9Y/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-DWYGFU@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T170000 DTEND;TZID=+07:20241216T171500 DESCRIPTION:Reservoirs play a crucial role in global water resource managem ent\, hydroelectric power generation\, and flood control. However\, their construction often entails significant ecological and socio-economic impac ts\, necessitating thorough environmental assessments. The Mekedatu Reserv oir Project\, situated on the Cauvery River in the Ramanagar district of K arnataka\, India\, holds paramount significance. Aimed at supplying the Be ngaluru Metropolitan Region and its surroundings with drinking water\, the project also endeavors to generate 400 MW of renewable energy annually. D espite its benefits\, the project comes with ecological costs\, as approxi mately 5252.40 hectares of revenue\, forest\, and wildlife land will be su bmerged. This necessitates a detailed evaluation of its potential environm ental consequences.\n\nThis study identifies a knowledge gap in the existi ng literature regarding the ecological implications of the Mekedatu Reserv oir Project. It seeks to fill this void by forecasting land use and land c over (LULC) changes for the years 2000\, 2010\, and 2020 using the Random Forest method\, and assessing the submergence area for different levels of the proposed reservoir. Catchment delineation is performed using the Soil and Water Assessment Tool (SWAT). Additionally\, the Cellular Automaton-M arkov Chain technique is employed to predict land use and land cover chang es for the year 2030. Integrating these methodologies\, the research provi des a holistic understanding of the project's environmental footprint.\n\n The land use and land cover analysis revealed significant shifts from 2000 to 2020\, with forest cover decreasing from 71.54% to 60.71% and barren l and increasing from 19.55% to 29.56%. The projected land use and land cove r for 2030 shows further forest reduction to 58.28% and barren land increa sing to 31.11%. These changes highlight a trend towards deforestation and land degradation\, posing severe ecological threats. The submergence area at the proposed reservoir Full Reservoir Level is estimated to be 5252.4 h ectares\, distributed as 6.62% water\, 19.55% barren land\, 71.54% forest area\, and 2.29% built-up area for the year 2000. The inundation of these areas will lead to significant biodiversity loss\, affecting numerous plan t and animal species.\n\nIn line with Sustainable Development Goals\, whic h advocates for sustainable water management\, this study emphasizes the i mportance of informed decision-making and sustainable development practice s. The findings underscore the need for new ecologically sensitive areas a nd the establishment of wildlife corridors\, conservation zones\, and affo restation programs to mitigate the adverse impacts. Continuous environment al monitoring and research are essential to track biodiversity impacts and adjust conservation strategies accordingly.\n\nPolicy implications of thi s study suggest that due process of law\, linked with the principle of nat ural justice\, must be adhered to in ensuring environmental balance. Recom mendations from the World Commission on Dams (WCD) highlight the need to r educe the negative impacts of dams by increasing the efficiency of existin g assets and minimizing ecosystem impacts. Policymakers must understand th e long-term ecological consequences of such mega projects and explore alte rnatives. Sustainable development models must be based on equality and nat ural justice.\n\nFuture research should focus on the socio-economic impact s of the Mekedatu Reservoir Project\, particularly the displacement of loc al communities. This includes conducting detailed socio-economic assessmen ts\, inclusive resettlement planning\, livelihood restoration programs\, a nd initiatives to preserve cultural heritage. Continuous monitoring and lo ng-term studies are crucial to ensure the well-being of resettled populati ons and to balance development with environmental and social sustainabilit y.\n\nIn summary\, this study advances the understanding of environmental impact assessment in reservoir projects\, providing valuable insights for stakeholders and policymakers. It highlights the critical need for sustain able development practices that ensure equitable access to water resources while preserving environmental integrity.\n\nKeywords: Environmental Impa ct Assessment\, Reservoir Project\, Machine Learning\, Random Forest\, Mar kov Chain\, Cellular Automaton\, Land Use Changes\, Submergence Area\, Sus tainable Development Goals\, Water Resource Management. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Predictive Analysis of LULC Dynamics for Area Under Submergence and its Environmental Impacts for the Mekedatu Reservoir Project - CHANDAN M C\, Pooja K\, Pratham Goudageri\, Vickey Rajendra Hegade\, Prithvi Raj Gow da S URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/DWYGFU/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-W3MLMN@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T170000 DTEND;TZID=+07:20241216T171500 DESCRIPTION:In recent years\, 3D city models have become crucial for urban planning and research. Japan's Project PLATEAU has led the development of open 3D city models and point cloud data\, with over 100 cities releasing Digital Twin data in CityGML format by February 2023. This talk explores t he collaboration between Japan's Digital Twin initiatives and the global O penStreetMap community. Since 2022\, Japan's Digital Twin data\, following the ODbL license\, has been integrated with OpenStreetMap using specially developed tools. This integration aims to promote the global adoption of 3D city models\, enhancing urban development through the synergy of Digita l Twin technologies and OpenStreetMap. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:The Current State of Collaboration between Digital Twin and OSM in Japan: A Case Study of Project PLATEAU - Taichi Furuhashi URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/W3MLMN/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-DFYYQM@talks.geoinfo-lab.org DTSTART;TZID=+07:20241216T170000 DTEND;TZID=+07:20241216T171500 DESCRIPTION:The ZOO-Project is an open-source processing platform released under the MIT/X11 Licence. It provides the polyglot ZOO-Kernel\, a server implementation of the Web Processing Service (WPS) (1.0.0 and 2.0.0)\, and the OGC API - Processes standards published by the OGC. It contains ZOO-S ervices\, a minimal set of ready-to-use services that can be used as a bas e to create more useful services. It provides the ZOO-API\, initially only available from the JavaScript service implementation\, which exposes ZOO- Kernel variables and functions to the language used to implement the servi ce. It contains the ZOO-Client\, a JavaScript API that can be used from a client application to interact with a WPS server. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:ZOO-Project: news about the Open Source Generic Processing Engine - Gérald Fenoy URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/DFYYQM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-MLEYUT@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T093000 DTEND;TZID=+07:20241217T094500 DESCRIPTION:In today's rapidly evolving field of map data visualization\, t he use of vector tiles is increasingly prevalent. Vector tiles offer flexi ble and efficient data display\, but they require JSON Style data to defin e their visual representation. JSON Style plays a crucial role in formatti ng data\, ensuring that presentations are both diverse and user-friendly. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:JSON Style Map: Enhancing Flexibility and Efficiency in Map Data Vi sualization - PEERANAT PRASONGSUK\, sattawat arab\, Arissara Sompita URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/MLEYUT/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-ANCWCT@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T093000 DTEND;TZID=+07:20241217T094500 DESCRIPTION:The SpatioTemporal Asset Catalog (STAC) revolutionizes geospati al applications by providing a standardized framework for cataloging spati otemporal data. Developed in 2017 through a collaborative effort among var ious organizations\, STAC streamlines the discovery and retrieval of geosp atial assets\, making it easier for users to access satellite imagery and other spatial data. This open-source specification\, which aligns with FAI R principles—Findable\, Accessible\, Interoperable\, and Reusable—prom otes interoperability among various data providers and applications\, fost ering innovation in the geospatial community.\n\nSTAC's design allows for automated data retrieval through the STAC API\, making it especially usefu l for applications in environmental monitoring\, disaster management\, and urban planning. Its JSON-based structure enhances user accessibility\, al lowing developers to quickly integrate geospatial data into their workflow s. Furthermore\, STAC's extensibility ensures it can adapt to a wide range of geospatial data types\, from remote sensing to 3D point clouds.\n\nThe benefits of STAC go beyond theoretical applications. In Thailand\, STAC i s applied to the GISTDA Decision Support System for Disaster Management Pl atform. On this platform\, STAC catalogs vector data related to flooding a reas\, thermal activities\, and drought indices. As a result\, the impleme nted application can efficiently browse and retrieve data from the STAC ca talog\, enhancing data retrieval speed and user experience.\n\nAs the geos patial landscape continues to evolve\, STAC stands out as a remarkable too l for driving innovation\, enabling seamless data sharing\, and empowering users to harness the full potential of geospatial technologies in address ing complex global challenges. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:STAC: Driving Innovation in Geospatial Applications - Siriya Saenkh om-or URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/ANCWCT/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-S9J8W3@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T093000 DTEND;TZID=+07:20241217T094500 DESCRIPTION:Environmental degradation in Southeast Asia\, particularly in C ambodia\, is an alarming issue that poses significant threats to both ecos ystems and human well-being. The region is experiencing rapid deforestatio n driven by agricultural expansion and urban development\, leading to subs tantial loss of forest cover. This deforestation disrupts ecological balan ce and biological environments\, contributing to habitat fragmentation\, b iodiversity loss\, and alterations in local microclimates. Concurrently\, changes in land use and land cover exacerbate these problems\, further fra gmenting habitats and impacting species distribution. The increasing risk of forest fires\, fueled by climate variability\, land use changes\, and a gricultural burning\, adds another layer of concern\, contributing to air pollution and posing risks to both natural and human systems.\n\nIn respon se to these growing challenges\, a comprehensive monitoring tool is essent ial for systematically observing and analyzing environmental parameters. S uch a tool would provide critical data necessary to address these issues\, inform policy decisions\, and support sustainable land management practic es. Recognizing the need for a robust solution\, SERVIR Southeast Asia (SE A)—a collaborative initiative of the U.S. Agency for International Devel opment (USAID)\, the National Aeronautics and Space Administration (NASA)\ , and the Asian Disaster Preparedness Center (ADPC)—has developed the “Biophysical Monitoring and Evaluation (M&E) Dashboard” for Cambodia. This open-access tool is designed to offer comprehensive\, near-real-time insights into critical environmental parameters\, leveraging advanced tech nologies to support environmental protection and sustainable management.\n \nThe Biophysical M&E Dashboard utilizes a range of cutting-edge technolog ies to analyze and visualize environmental data. It harnesses the power of Google Earth Engine (GEE)\, a cloud computing platform capable of process ing vast amounts of satellite imagery. GEE is employed to analyze large-sc ale\, open satellite data to map key indicators such as the Enhanced Veget ation Index (EVI)\, land use and land cover\, forest cover\, forest fire o ccurrences\, and crop monitoring. The tool integrates these insights with data from GeoServer\, an open-source geospatial data publisher\, and Postg reSQL\, a powerful open-source relational database system. Modern web tech nologies\, including React with NextJS and the Python-based Django framewo rk\, are used to develop the user interface and ensure seamless functional ity.\n\nThe M&E Dashboard is designed to integrate multiple data sources\, providing a holistic view of landscape dynamics. It visualizes and analyz es critical aspects such as forest cover changes\, land use transformation s\, vegetation health\, and fire hotspots. By offering detailed analytical information at various levels—country\, province\, district\, and desig nated protected areas—the tool enables users to gain a nuanced understan ding of environmental trends. The dashboard’s current capabilities inclu de monitoring forest gain and loss\, assessing rice cropping fields\, and tracking deforestation and fire hotspots. Future plans include expanding i ts functionality to support user-defined area levels and the incorporation of socio-economic and vulnerability indicators\, further enhancing its ad aptability and utility.\n\nIn addition to land use and land cover monitori ng\, the M&E Dashboard incorporates weather and climate information to sup port sustainable agriculture practices. This integration provides valuable insights into the impacts of climatic conditions on crop health and agric ultural productivity\, and also a drought assessment framework\, aiding in the development of strategies to mitigate adverse effects and enhance res ilience.\n\nThis paper presents a detailed overview of the architecture\, design\, and functionality of the Biophysical M&E Dashboard. It outlines h ow the tool addresses critical environmental challenges in Cambodia\, incl uding deforestation\, habitat fragmentation\, and fire risks. By offering a comprehensive suite of analytical features and visualizations\, the dash board supports informed decision-making and strategic planning for sustain able landscape management. Through its integration of advanced technologie s and multi-source data\, the Biophysical M&E Dashboard stands as a vital resource for protecting Cambodia’s natural resources and promoting ecolo gical resilience in the face of ongoing environmental pressures. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:SERVIR Southeast Asia Biophysical M&E Dashboard: A Tool to Support Landscape Monitoring - MD KAMAL HOSEN URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/S9J8W3/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-C9LSPS@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T094500 DTEND;TZID=+07:20241217T100000 DESCRIPTION:In the past\, the installation of GIS applications often encoun tered challenges regarding the flexibility of services\, which were unable to be scaled to accommodate a growing number of users. The interconnectio n and exchanging of data across services were constrained\, and service se paration was not feasible. These issues had a significant impact on overal l usability.\n\nThe design and deployment of applications in the form of m icroservices are gaining popularity and widespread adoption. This approach aims to provide flexibility to the installation process\, allowing servic es to be added or reduced as needed to align with usage requirements. It c an subdivide services into smaller units to facilitate installation\, foll owing the principles outlined in The Twelve-Factor App (https://12factor.n et/).\n\nNowadays\, GIS application development has OGC Standards\, which are standardized guidelines that define the process of storing and providi ng geospatial data. These standards encompass a multitude of aspects of ge ospatial information interoperability. Therefore\, the principles of The T welve-Factor App can be adapted to the design and deployment of GIS applic ations\, ensuring compliance with OGC Standards.\n\nThis session will elab orate on how the principles of The Twelve-Factor App can be harmonized wit h OGC Standards\, as well as the various technologies selected for the des ign and deployment of applications. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Design and Deploy Microservice for GIS Application apply OGC Standa rd - Worrathep Somboonrungrod URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/C9LSPS/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-CTJAG8@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T094500 DTEND;TZID=+07:20241217T100000 DESCRIPTION:Air pollution in Southeast Asia has reached critical levels\, s ignificantly impacting human health across the region. Nearly the entire p opulation lives in areas where air pollution exceeds the World Health Orga nization’s (WHO) safe air standards. This severe pollution is primarily due to rapid industrialization\, urbanization\, and deforestation\, which have increased the amount of harmful pollutants in the air\, particularly fine particulate matter known as PM2.5. Seasonal agricultural burning\, a common practice in the region\, also contributes significantly by releasin g large quantities of smoke and particulate matter into the air. The rapid pace of urbanization has led to increased vehicle emissions and construct ion activities\, further degrading air quality. \n\nTo address this issue\ , SERVIR Southeast Asia (SEA)\, a joint initiative of the U.S. Agency for International Development\, the National Aeronautics and Space Administrat ion (NASA)\, and the Asian Disaster Preparedness Center (ADPC) — its imp lementing partner\, has developed the "SERVIR Southeast Asia Air Quality E xplorer" to monitor air pollution and health impacts using satellite data and atmospheric modeling. The application uses advanced data visualization techniques to present complex datasets in an accessible manner. By harnes sing the power of satellite data and predictive models\, we hope that the SERVIR Southeast Asia Air Quality Explorer (SEA AQE) serves as a valuable resource for policymakers\, researchers\, and the general public\, empower ing them to make informed decisions to mitigate the adverse effects of air pollution.\n\nThe Air Quality Explorer features a user-friendly interface accessible on both desktop and mobile devices\, allowing users to monitor real-time air pollution levels\, including three-day forecasts of PM2.5 w ith a 5 km resolution and NO2 from Geostationary Environment Monitoring Sp ectrometer (GEMS). The application also features a fire hotspot map\, help ing users anticipate changes in air quality. It ranks cities over the Sout heast Asia regions based on their PM2.5 levels and integrates PM2.5 data w ith a health index to translate the data into actionable health recommenda tions. Additionally\, the tool includes six-hourly forecast wind data from NOAA and ground station data from Thailand’s Pollution Control Departme nt (PCD)\, offering a comprehensive view of air quality dynamics across th e region.\n\nThis project highlights the potential of combining satellite technology and forecast modeling with web-based platforms to improve envir onmental monitoring and decision-making in Southeast Asia. SERVIR SEA and collaborators will continue to enhance this tool with new useful data\, su ch as high resolution of forecast PM2.5\, fire risk\, and fire emission in ventory products\, enabling users to link and analyze these with air pollu tion indicators. Additionally\, the power of large language models (LLMs) will be applied to this tool\, allowing users to input queries in natural language. This feature will translate user input into data retrieval comma nds\, providing users with the desired results and making the tool even mo re accessible and user-friendly. Furthermore\, we will develop “SERVIR S EA AQ API” service to provide air pollution satellite image data and jso n format data for integration on other platforms. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:SERVIR Southeast Asia Air Quality Explorer: A Tool Harnessing Satel lite and Modeling Data for Pollutant Monitoring in the Region - Thannarot Kunlamai URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/CTJAG8/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-BFP8PP@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T094500 DTEND;TZID=+07:20241217T100000 DESCRIPTION:Introduction/Background:\nThe consequences of climate and envir onmental changes on health are now obvious to communities\, institutions a nd researchers alike. The impact of these changes must now be considered i n an operational way in health management\, in order to anticipate their e ffects\, prevent them or mitigate them where possible. In practice\, there is very little routine real-time use of space observation data in the pub lic health sector\, despite the increasing availability of space data. Ind eed\, space observation technologies have been constantly evolving since t he 1970s\, and now offer a wide range of data at different spatial\, tempo ral and radiometric resolutions. More recently\, access to data acquired b y satellite has greatly improved\, with free\, massive data and easier pro cessing. This offers the possibility of supporting health surveillance at various scales\, which will be explored in this presentation using differe nt examples from South-East Asia.\n\nMain Aim/Purpose:\nThis presentation aims at raising the issue of integrating environmental and climate change indicators into health monitoring\, by presenting practical tools and case studies that are operational in Southeast Asia. It will provide an update on the needs for further operational implementation that will benefit hea lth monitoring.\n\nMethodology and Findings:\nThe presentation will focus firstly on the development of a web platform that aims at modeling suitabl e climate and environmental conditions for leptospirosis through Earth obs ervation\, over the agglomeration of Yangon\, Myanmar. Leptospirosis is a bacterial zoonosis that remains rarely diagnosed in Southeast Asia despite a high morbidity as shown in several active investigations. It is strongl y associated to water and seasons with epidemics following heavy rainfall and flooding episodes. In the frame of the ECOMORE 2 Project (coordinated by Institut Pasteur and funded by the French Agency for Development - AFD) \, the locations of leptospirosis confirmed cases (vs non-leptospirosis co ntrols) included in 2019 and 2020 were analyzed retrospectively. Time seri es of vegetation\, water\, and moisture indices from Sentinel-2 satellite imagery (available at 10 meters spatial resolution\, every 5 days\, from t he European Space Agency\, Copernicus Program) were produced to describe t he dynamics of the environment around the locations of residence. This pro cess relies on the use of the Sen2Chain processing chain developed in Pyth on and open (https://framagit.org/espace-dev/sen2chain). The most relevant indices were used to build a spatiotemporal prediction model of positive vs negative locations. This model was spatialized on homogeneous landscape units from the point of view of land use\, and describing the whole study area. The acquisition of Sentinel-2 images\, their processing and the mod elling were then automated as soon as a new image is available (every 5 da ys). An online platform\, named LeptoYangon (https://leptoyangon.geohealth research.org/)\, was developed with R and R-Shiny to display this dynamic mapping of suitable environments and inform the epidemiologists and physic ians of the study\, in the frame of the ClimHealth project (funded by CNES and accredited by the Space Climate Observatory International Initiative) . This fully automated tool allows retrospective consultation at any date since the first Sentinel-2 image was available in March 2016 (over 7 years ). By clicking on the map\, the user can select a landscape unit and view the temporal dynamics of the risk for that unit (i.e. whether the risk is increasing and decreasing). The user can also view the vegetation\, water and moisture indicators to get an idea of the environmental data more spec ifically. This platform was designed to be used by epidemiologists and phy sicians to visualize the most at-risk areas and those where the risk is in creasing\, in order to raise physicians' awareness of leptospirosis (often confused with other fevers).\n\nDiscussion:\nImplementing this tool in ot her territories is facing 1) methodological challenges regarding the volum e of satellite data to be processed and 2) the need for detailed knowledge of the ecology of leptospirosis and exposure factors to adapt the models in different contexts. However\, this already operational tool opens the w ay to the development of climate and environmental monitoring systems to i ncrease the vigilance of healthcare workers and populations to the risk of leptospirosis. This also shows the relevance of developing specific tools for other diseases associated to climate and environment. At a country or regional scale\, it is mainly meteorological variations and climatic anom alies that are relevant to the surveillance of certain diseases\, such as dengue fever. The presentation will finally review the development of a na tional early warning system in Cambodia based on the acquisition of such c limatic data. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Geospatial climate and environmental monitoring for health surveill ance - Vincent HERBRETEAU URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/BFP8PP/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-TSAVMQ@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T094500 DTEND;TZID=+07:20241217T100000 DESCRIPTION:Southeast Asia (SEA) region leads the world in palm oil product ion\, with Indonesia\, Malaysia\, and Thailand collectively contributing o ver 88% of the global production. However\, the tropical climate in the SE A region resulted in oil palm trees vulnerable to various diseases such as Fatal Yellowing (FY) and Ganoderma boninense. To keep track of productivi ty\, it is crucial to monitor the varying conditions of oil palm trees—s uch as healthy\, dead\, yellow\, and small—and apply effective pruning t echniques to cure affected trees. Manual approach for oil palm tree detect ion is expensive\, tedious\, and prone to inaccuracies. Thus\, our study i s focused to automate the detection of oil palm trees and their states usi ng a deep learning (DL) algorithm on unmanned aerial vehicle (UAV) imagery . We use YOLOv8\, one of the latest open-source models\, on the publicly a vailable UAV dataset\, named MOPAD\, containing training and validation se ts. The performance of the model is further compared with other state-of-t he-art object detectors. In addition\, a prototype web application is deve loped to demonstrate the robustness of the model in adverse real-world con ditions and for potential deployment. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Multi-Class Oil Palm Tree Detection from UAV Imagery Using Deep Lea rning - Aakash Thapa\, Teerayut Horanont URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/TSAVMQ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-WN8GE7@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T100000 DTEND;TZID=+07:20241217T101500 DESCRIPTION: DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Sen2Extract: A Free Online Tool to Access Environmental Index Time Series from Sentinel 2 - George Ge URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/WN8GE7/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-EKNY9X@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T100000 DTEND;TZID=+07:20241217T101500 DESCRIPTION:Geospatial Topology\, a fundamental concept in geographic infor mation systems\, focuses on the analysis and characterization of spatial r elationships between geographic entities without alteration of their intri nsic properties. This presentation examines the implementation of Geospati al Topology rules utilizing Python programming language\, facilitating exe cution in both client-side and server-side environments.\nWe present an em pirical case study from the PWA GIS Department of Thailand\, which employs a comprehensive set of over 30 topology rule-based validations to ensure data integrity and consistency across national cartographic operations. Th e research investigates two primary platforms: QGIS Desktop and Web Applic ations\, both serving as client-side interfaces capable of executing topol ogy scripts and generating inconsistency reports for subsequent rectificat ion.\nA critical distinction between QGIS Desktop and Web Application lies in their respective execution paradigms: QGIS Desktop operates within a l ocal environment\, while the Web Application leverages server-side process ing capabilities. \nThis presentation will elucidate methodologies for dev eloping custom topology rules and demonstrate techniques for accessing sin gle-algorithm Python scripts across both Desktop and Web Application envir onments. By bridging the technological gap between these platforms\, we ai m to enhance the efficacy of geospatial data quality control processes and optimize GIS workflows.\nThe findings of this research contribute to the broader understanding of cross-platform geospatial topology implementation and offer practical insights for GIS professionals and developers seeking to improve data validation processes in diverse computational environment s. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Server-Side and Client-Side Topology rule-based by python from Prov incial Waterworks Authority - PEERANAT PRASONGSUK\, NATPAKAL MANEERAT URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/EKNY9X/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-HGLKZM@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T100000 DTEND;TZID=+07:20241217T101500 DESCRIPTION: DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Scrollytelling the 53 Stations of Tōkaidō: An Interactive Journey Through Japan’s Historic Route - Sorami Hisamoto URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/HGLKZM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-S8VH7C@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T100000 DTEND;TZID=+07:20241217T101500 DESCRIPTION:Spaceborne hyperspectral data can assist in estimating crop yie lds\, predicting crop outcomes\, and monitoring crops\, which ultimately c ontributes to loss prevention and food security. Recently\, EnMAP hyperspe ctral imagery has become available\, starting from 2022. This study aims t o analyze and classify oil palm and nipa palm plantations using hyperspect ral images combined with machine learning algorithms. The Random Forest cl assifier\, CatBoost classifier\, and LightGBM classifier were utilized to automatically map the oil palm and nipa palm areas. The fully workflows of the hyperspectral imaging process were performed in EnMAP-Box plugin on Q GIS software. The overall accuracy of three ML classfiers provides greater than 90% especially in oil palm and nipa palm plantations. Machine learni ng can find out the hidden information about spectral characteristics. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Hyperspectral Remote Sensing Data Analysis for Oil Palm and Nipa Pa lm Plantation Using EnMAP-Box open-source plugin on QGIS - Jirawat Daranee srisuk URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/S8VH7C/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-NJXT8V@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T101500 DTEND;TZID=+07:20241217T103000 DESCRIPTION:A part of the web map application\, ensuring functionality and accuracy is paramount to delivering a reliable user experience. This abstr act outlines a systematic approach to expectation testing for web maps\, f ocusing on validating that these applications meet predefined criteria and user expectations.\n\nExpectation testing involves setting specific crite ria that web map applications must meet to ensure they perform as intended . This process includes validating map data accuracy\, user interface resp onsiveness\, and the effectiveness of interactive features such as zooming \, panning\, and layer management. The goal is to ensure that the web map not only displays information correctly but also responds appropriately to user interactions and integrates seamlessly with other system components. \n\n\n\nBy implementing a robust expectation testing framework\, developer s can identify and address potential issues before deployment\, ensuring t hat web map applications deliver accurate and efficient performance. This process not only enhances the quality of the application but also builds u ser trust by meeting or exceeding their expectations. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Expectation Testing for Web Map Applications - Parichat Namwichian URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/NJXT8V/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-DTYBZN@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T101500 DTEND;TZID=+07:20241217T103000 DESCRIPTION:Geospatial big data plays a pivotal role in the context of smar t grids\, revolutionizing the way modern electrical grids are monitored\, managed\, and optimized. Smart grids integrate advanced sensing\, communic ation\, and control technologies to enhance the efficiency\, reliability\, and sustainability of electricity distribution. While locational informat ion of the smart meters is pivotal\, the consumption patterns combined wit h other information like consumer type\, land use and local weather condit ions can really enhance the assessment of energy requirements and usage th us leading to a Spatial Energy Management System (SEMS). This will signifi cantly enrich location-aware decision-making\, real-time monitoring\, and predictive analysis\, thus improving energy resource optimisation and achi eving the goal of SDG-7. The SEMS web application represents a critical ad vancement\, facilitating dynamic visualization of temporal clusters\, acro ss energy sources and their evolution over time. By overlaying clusters ac ross different time periods\, utility personnel gain insights into custome r categorizations vs actual utilization\, enabling understanding of demand fluctuations\, outages and faults\, fault localization within the electri c network and demand-generation assessment of renewable energy. \nThe prim ary objective is the development of a dynamic web-based SEMS application c apable of visualizing temporal changes and consumption patterns\, while al so providing alerts to facilitate proactive management.\n\nThe initial pha se of the methodology focuses on the Jeedimetla region in Hyderabad\, leve raging real-world data encompassing 6\,000 households categorized into res idential\, commercial\, and industrial segments. Quantum Geographic Inform ation System (QGIS) software is employed to establish the electric network . To store the spatial and temporal consumption data\, the data storage in frastructure employs PostgreSQL\, enhanced with the PostGIS extension. Thi s combination is selected due to its robust capabilities in accommodating and managing complex datasets. Additionally\, a comprehensive and refined data model has been established to serve as the framework for storing Adva nced Metering Infrastructure (AMI) data. Spatial data retrieved from the P ostgreSQL database is exposed through a Web GIS Server (GeoServer) as a We b Feature Service\, facilitating its integration into applications. This s patial information is utilized within the OpenLayers Software Development Kit (SDK) to visually render data within a JavaScript-based application en vironment. Non-spatial data is accessed through Application Programming In terfaces (APIs) integrated within a Node server\, operating as REST endpoi nts. Concurrently\, the React JavaScript library is employed to present th is non-spatial information to end-users in an interactive format.\n\nThe S EMS Geospatial Visualization Engine comprises three key components. The fi rst component features two primary views: the Basic View\, which presents hourly usage consumption data\, and the Combined View\, integrating map an d graph interfaces. This combined view allows users to select specific tim e periods\, customer locations\, transformers\, or feeders\, with the grap h view displaying corresponding hourly consumption data. Both views facili tate the visualization of consumption patterns for customer classes or typ es\, which are pre-defined in the system.\nThe second component of the SEM S Visualization Engine aggregates data at the daily level\, classifying us ers into low\, medium\, and high consumption categories. These classificat ions are visually represented on the map view\, providing insights into co nsumption trends across the study area.\nThe third component integrates we ather data sourced from an open-source Weather API with customer energy co nsumption data. Weather information is stored in the PostgreSQL database\, and the combined view enables the graphical representation of weather dat a changes alongside energy consumption data. This integration enhances und erstanding of how weather fluctuations impact customer energy usage patter ns.\n\nBy leveraging advanced sensing\, communication\, and control techno logies\, three components of SEMS Geospatial Visualization engine framewor k provides a comprehensive platform for understanding and visualizing ener gy consumption and demand. The spatial view aids in developing spatial clu sters\, which assist electric utility personnel in comprehending consumpti on patterns and energy demand requirements for specific areas\, facilitati ng efficient management of power shortage scenarios.\n\nThe SEMS visualiza tion engine supports various utility use cases\, including energy loss ide ntification\, detection of energy overuse\, and dynamic reclassification o f users based on current consumption data. As a prospective avenue for fut ure research\, integrating this Geospatial Visualization Engine framework with machine learning-based prediction models holds promise for forecastin g energy consumption and dynamically classifying users. Such advancements are anticipated to further empower utility personnel in decision-making\, real-time monitoring\, and predictive analytics within smart grid environm ents. DTSTAMP:20260416T211403Z LOCATION:Room34-1102 SUMMARY:Visualizing and Managing Smart Grids with Geospatial Big Data: The SEMS Approach - Venkata Satya Rama Rao Bandreddi URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/DTYBZN/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-UUUPDK@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T101500 DTEND;TZID=+07:20241217T103000 DESCRIPTION:Species Distribution Modeling (SDM) is a statistical methodolog y used to predict the spatial and temporal distribution of species based o n environmental conditions that are conducive to their survival and reprod uction. This modeling approach leverages spatially explicit species occurr ence records alongside various environmental covariates\, including climat e\, terrain\, and land cover\, as input variables\, with the aim of quanti fying and mapping species-environment interactions. SDM has become a criti cal tool in ecological research and conservation biology for understanding and predicting species distribution patterns. A range of machine learning and deep learning techniques can be employed in SDM\, such as Logistic Re gression (LR)\, Random Forest (RF)\, Multilayer Perceptron (MLP)\, Convolu tional Neural Network (CNN)\, and Generative Adversarial Network-CNN (GAN- CNN). Despite the availability of these techniques\, there is a lack of a comprehensive application that integrates these algorithms for species dis tribution modeling. To address this gap\, this paper introduces a new tool \, kari-sdm\, which enables users to perform SDM utilizing a variety of te chniques. Kari-sdm supports LR\, RF\, MLP\, CNN\, and GAN-CNN algorithms\, all based on open-source frameworks PyTorch and scikit-learn. Additionall y\, it facilitates all necessary preprocessing steps\, from data collectio n\, cleaning\, transformation\, spatial preprocessing\, and environmental variable selection\, to data splitting. The tool also provides functions f or model evaluation\, result visualization\, and cross-validation. The pri mary goal of kari-sdm is to assist ecologists in modeling species distribu tions\, interpreting results\, and developing informed conservation and ma nagement strategies. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:kari-sdm: Advanced Species Distribution Modeling using PyTorch and scikit-learn - Lee\, Jeongho\, Byeong-Hyeok Yu\, Chunghyeon Oh\, Soodong L ee\, Cho Bonggyo URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/UUUPDK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-3HL8X9@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T101500 DTEND;TZID=+07:20241217T103000 DESCRIPTION:The French National Geographic Institute (IGN) came to Cambodia between 1952 and 1954 to carry out a large-scale photographic project\, t aking around 11\,000 aerial images over a large part of the country. \n\nT oday\, this collection represents an exceptional archive that takes us bac k 70 years to the urban and rural landscapes of the time. In addition to t hese early aerial photographs\, there is a higher resolution shot of Phnom Penh in 1993\, with incredible details of life in the streets of the capi tal. This archive is of great interest in many fields\, including history\ , geography\, archaeology\, urban planning\, and ecology. The purpose of t he project is to dematerialize this archive and make it accessible and usa ble free of charge in Cambodia. \n\nAll the images were digitized and supp lied to the KHmer Earth OBServation (KHEOBS) laboratory to create orthopho tographs. Processing has been completed for the municipality of Phnom Penh \, for the years 1953 and 1993. In order to make these images viewable by anyone\, a web application\, GeoCambodia\, was developed to visualize Camb odia then (past) and now (present). The user-friendly interface includes a n interactive slider to navigate and compare the old 1953 and 1993 aerial orthophotographs with the recent Google Earth images. Also\, vector outlin es of buildings from 1993\, produced by the Atelier Parisien d’Urbanisme (APUR)\, have been integrated to enable visitors to click on a building a nd view the APUR’s descriptive architectural sheets. Other functions and the extension of the aerial images to the whole of Cambodia are still to come in this interface.\n\nGeoCambodia.org targets anyone who is intereste d in aerial and satellite imagery and how Cambodia evolves through time an d space\, especially geography enthusiasts. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:GeoCambodia: A web application to visualize Cambodia Then and Now t hrough aerial photographs and satellite images. - Chamroeun YORNGSOK URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/3HL8X9/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-W7J3LW@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T110000 DTEND;TZID=+07:20241217T111500 DESCRIPTION:Open spatial data plays a transformative role in the landscape of higher education in Thailand\, bridging the gap between theoretical lea rning and practical application in daily life. This keynote address will e xplore how open spatial data is being integrated into the higher education curriculum\, emphasizing its significance in enhancing students' understa nding of geography\, urban planning\, environmental management\, and relat ed disciplines. \n\nIllustrating how open spatial data is increasingly in fluencing daily life in Thailand through community-driven mapping projects \, public health initiatives\, or sustainable development planning\, open spatial data is empowering. The session will also address the challenges a nd opportunities in the widespread adoption of open spatial data within hi gher education\, including issues of data quality and accessibility and th e need for ongoing support and collaboration between academic institutions \, government agencies\, and the private sector. Ultimately\, this session aims to inspire educators\, students\, and professionals to harness the p ower of open spatial data\, transforming education and society at large in Thailand. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Open spatial data in Thailand Higher Education Context - Classroom to Daily Life - Chomchanok Arunplod URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/W7J3LW/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-FX9YYW@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T110000 DTEND;TZID=+07:20241217T111500 DESCRIPTION:Geographic information technology has become an important part of our daily lives\, leading to the development of various map application s. The design of these applications requires special attention\, particula rly in terms of User Experience (UX) and User Interface (UI) design. Effec tive UX/UI design significantly impacts user satisfaction and ease of use\ , contributing to the overall efficiency and modernity of map applications . By understanding user needs through User Research and Usability Testing\ , we can establish principles and guidelines for creative design that enha nce usability and ensure consistency between data presentation and user in teraction.\n\nCreating a map application that offers a good user experienc e involves careful design of map elements\, such as the layout of basic ma p tools\, the selection of symbols\, the arrangement of data\, and the des ign of interactions. The design must be suitable for different types of us age\, such as travel applications\, survey applications\, or application f or specific use. Additionally\, it must support various devices\, includin g mobile phones\, computers\, tablets\, and other devices with different s creen sizes.\n\nFurthermore\, techniques that can be applied in the design process are presented in this session to achieve the best results. This i ncludes creating an immersive user experience by strategically using color s\, fonts\, and layout. Developing engaging and interesting ways to displa y information will help users feel more connected to the application. It i s also important to stay updated with current trends in map application de sign to ensure that the developed applications are modern and responsive t o global changes.\n \nThis presentation is suitable for designers\, develo pers\, and anyone interested in geographic information technology\, especi ally those involved in developing map applications. It will provide design guidelines that can be applied to various projects\, effectively meeting the diverse needs of future users. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Designing user experience and user interface for effective map appl ications. - jirayut Narksin\, Nichaphat Hongkeaw\, Mayurachat Saechan URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/FX9YYW/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-9WESSG@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T110000 DTEND;TZID=+07:20241217T111500 DESCRIPTION:In this session\, I will introduce a module I developed for clu stering icon image markers using MapLibre\, with the theme of "finding nea rby restaurants that look delicious." This is the first module I’ve ever created\, and it’s designed with a very simple structure. Through this session\, I hope participants will take away two key messages: that creati ng what you want to make is enjoyable\, and that even if you’re not high ly skilled\, sharing your work can lead to valuable learning experiences. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Enjoying Delicious Meals Using MapLibre: A Journey into Developing a MapLibre Module - Shinsuke Nakamori URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/9WESSG/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-THZSUG@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T111500 DTEND;TZID=+07:20241217T113000 DESCRIPTION:This study aims to find the results of using a free disaster ma pping application developed on Google Earth\, named “Hazmapper”. This tool allows users to create maps and GIS products from Sentinel or Landsat datasets without the high time and cost usually needed for traditional an alysis. \n\nThe initial design of the HazMapper program used indicators ba sed on the Normalized Difference Vegetation Index (NDVI). Specifically\, i t developed the relative difference NDVI (rdNDVI) to identify areas where vegetation was removed after natural disasters. Because these indicators r ely on vegetation\, HazMapper is unsuitable for desert or polar regions\, which means appropriate for tropical areas. \n\nUsing the rdNDVI indicator for different years in the same area and comparing the average absolute e rror (MAE) of all results to test the effectiveness of the Hazmapper model in application to flooded areas. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:USING FOSS4G TOOLS WITH RDNDVI TECHNIQUES TO ANALYZE FLOOD HAZARD I N TROPICAL SE ASIA AREA AT WANG THONG RIVER BASIN\, PHITSANULOKE\, THAILAN D. - Kittituch Naksri | Chaiwiwat Vansarochana URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/THZSUG/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-3U9UAM@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T111500 DTEND;TZID=+07:20241217T113000 DESCRIPTION:Global Ecosystem Dynamics Investigation (GEDI) and Ice\, Cloud\ , and Land Elevation Satellite 2 (ICESat-2) are earth observation missions from NASA to construct a three-dimensional model of earth surface in spac e and time empowered by Light Detection and Ranging (LIDAR). GEDI and ICES at-2 data are organized by orbit ID\, sub-orbit granule and track\, and di stributed in HDF5 format\, which is optimized for big data storage. Howeve r\, this approach is inconvenient for extracting spatio-temporal areas of interest\, because each file stores a track crossing a huge range of latit ude and longitude\, while lacking a spatial index.\n\nTo facilitate random access to small areas of interest\, we propose a data reconstruction proc ess through Apache Parquet. Parquet is an open source column-oriented data format designed for efficient data storage and retrieval. We sequentially stream raw data into spatio-temporal partitioning blocks (5 degree x 5 de gree x year). This layout optimizes the number of partitions (n = 3337) a nd individual file size (~300 MB). Independence of raw data files and a pr edefined partititoning scheme enables parallel processing\, and periodic u pdate while new data is available.\n\nDuring the reconstruction\, we selec ted essential attributes and applied quality filtering based on scientific literature. We excluded GEDI shots with Quality Flag equal to 0\, Degrada tion Flag larger than 0\, or Sensitivity smaller than 0.95\; For ICESat-2 ATL08\, we first excluded segments where terrain and/or canopy height are in NaN. We then reconstructed individual photons from ATL03 by ph_segement _id\, and excluded the ones classified as noise\, as well as segments cont aining more than 28 photons\, according to the result from previous resear ch [1]. \n\nData is finally converted to GeoParquet and published on a clo ud server under CC-BY 4.0 license. GEDI Level2 has 1.4 TB\, and ICESat-2 A TL08 has 3.8TB in total size respectively. GeoParquet supports two levels of predicate push down: first\, at the partition level\, and second\, at t he file level. The partitioning of the global LiDAR datasets enables coars e spatial (5 x 5 degree) and temporal (year) filtering.. The footer of eac h GeoParquet file enables spatial filtering via bounding boxes or geometry features\, and temporal filtering using the datetime columns. Further att ribute filtering is possible.\n\nThe concept of Analysis-Ready Cloud Optim ized (ARCO) data has been defined and implemented for raster data\, using technologies such as Zarr or Cloud Optimized GeoTiff (COG) [2]. However\, corresponding implementations for vector data are scarce. This work delive rs two instances of global ARCO vector datasets. It not only adheres to th e concept of 4C (complete\, consistent\, current\, and correct)\, but also tackles the challenge of organizing terabyte-scale geospatial vector data .\n\nReference\n\nMilenković\, M.\, Reiche\, J.\, Armston\, J.\, Neuensch wander\, A.\, De Keersmaecker\, W.\, Herold\, M.\, & Verbesselt\, J. (2022 ). Assessing Amazon rainforest regrowth with GEDI and ICESat-2 data. Scien ce of Remote Sensing\, 5\, 100051.\nStern\, C.\, Abernathey\, R.\, Hamman\ , J.\, Wegener\, R.\, Lepore\, C.\, Harkins\, S.\, & Merose\, A. (2022). P angeo forge: crowdsourcing analysis-ready\, cloud optimized data productio n. Frontiers in Climate\, 3\, 782909. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Building an Analysis-Ready Cloud Optimized Global Lidar Data (GEDI and ICESat-2) for Earth System Science applications - Yu-Feng Ho URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/3U9UAM/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-UNZN7S@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T111500 DTEND;TZID=+07:20241217T113000 DESCRIPTION:The rapid development of autonomous vehicles (AVs) demands stro ng perception systems capable of reliably recognizing and classifying obje cts in complicated urban environments. To improve the reliability and prec ision of 3D object identification\, combining camera and LiDAR sensors has emerged as a potential solution. This research describes a multimodal fus ion framework that uses camera pictures and LiDAR point clouds to accompli sh high-performance 3D object recognition in urban circumstances. Camera s ensors provide precise color and texture information necessary for identif ying traffic signs\, pedestrians\, and cars\, whereas LiDAR provides exact depth measurements required for interpreting object geometry and spatial relationships. The indicated fusion technique improves detection accuracy by using these sensors' enhancing strengths\, especially in difficult sett ings such as occlusions and fluctuating illumination conditions taking the KITTI open dataset. Here the OpenPCDet is used for 3D object detection an d MMDetection for 2D detection as open library. Beside this the Facebook A I Research\, Detectron2 flexible framework for 2D and 3D object detection tasks is more popular too. \n\nFusion is accomplished via a well built arc hitecture that aligns and combines data from both modalities at various st ages of the detection pipeline\, such as feature extraction\, region propo sal\, and classification. Advanced deep learning techniques\, such as conv olutional neural networks\, are used to process and integrate multimodal i nput. Experimental results show that 3D object detection outperforms singl e-modality techniques in terms of robustness and precision\, particularly when recognizing small and partially occluded objects. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Camera-LiDAR Fusion for multimodal 3D Object detection in Autonomou s Vehicles - Badri Raj Lamichhane URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/UNZN7S/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-97UWEY@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T113000 DTEND;TZID=+07:20241217T114500 DESCRIPTION:It is crucial for enhancing the efficiency and accessibility of land use and land cover monitoring\, supporting informed decision-making in urban planning\, environmental management\, and sustainable development across diverse geographical contexts. This aims to streamline the process of satellite data acquisition\, preprocessing\, and seasonal LULC classif ication through the integration of artificial intelligence (AI) models.\n\ nThe proposed system will consist of two main components: (1) an automated satellite data fetching and preprocessing module\, and (2) an AI-driven L ULC classification module. The first component will leverage open-source t ools to access and prepare satellite imagery from freely available sources \, such as Landsat and Sentinel missions. This module will handle tasks in cluding data download\, atmospheric correction\, cloud masking\, and image compositing.\n\nThe second component will employ state-of-the-art machine learning algorithms\, particularly deep learning models\, to perform seas onal LULC classification. The system will be trained on diverse datasets t o recognize and categorize various land cover types across different seaso ns\, accounting for temporal variations in vegetation\, urban expansion\, and other dynamic landscape features.\n\nBy automating these processes\, t he proposed system aims to significantly reduce the time and expertise req uired for LULC analysis\, making it more accessible to researchers\, urban planners\, and environmental managers. The use of free and open-source so ftware ensures that the developed tools will be widely available and custo mizable for different geographical contexts and research needs.\n\nThis co ntributes to the advancement of remote sensing applications and supports i nformed decision-making in land management\, urban planning\, and environm ental conservation efforts. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:Land Use Land Cover Classification Automation Development using Fre e and Open-Source Software - Thantham Khamyai URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/97UWEY/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-SWHW7J@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T113000 DTEND;TZID=+07:20241217T114500 DESCRIPTION:Hydrometeorological data are crucial for effective water resour ce management\, weather forecasting\, and climate adaptation. In Asia\, a region known for its vast geographic diversity and varied climatic conditi ons\, the lack of high-resolution data has been a significant challenge fo r addressing local-scale issues. Traditional datasets often have coarse sp atial resolution (e.g.\, 10 – 25 km)\, limiting their usefulness for det ailed\, localized analysis. To address this gap\, we have developed a pion eering dataset offering 1 km resolution hydrometeorological data for the e ntire Asian continent. This dataset includes essential variables such as p recipitation\, surface temperature\, radiation\, soil moisture\, evapotran spiration\, groundwater\, and surface runoff delivering unprecedented deta il and accuracy compared to existing coarse-resolution data. The dataset w as created using advanced remote sensing techniques\, land surface physics \, and sophisticated data assimilation methods\, ensuring both enhanced sp atial resolution and accurate reflection of local conditions. Our dataset spans from 1940 to the present\, providing a comprehensive historical arch ive and seasonal forecasts extending up to six months into the future. Thi s combination of historical and predictive data makes it an invaluable res ource for a variety of applications\, including water resources\, climate studies\, agriculture\, and disaster assessment. To validate the accuracy of our high-resolution data\, we conducted extensive comparisons with sate llite remote sensing products such as MODIS (Moderate Resolution Imaging S pectroradiometer)\, GRACE (Gravity Recovery and Climate Experiment)\, and SMAP (Soil Moisture Active Passive). These comparisons confirm that our da taset offers superior accuracy and finer detail compared to publicly avail able data. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Advancing Hydrometeorological Data in Asia for Enhanced Water Resou rces and Climate Applications - Natthachet Tangdamrongsub URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/SWHW7J/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-TLBAPD@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T113000 DTEND;TZID=+07:20241217T114500 DESCRIPTION:This study explores the integration of Natural Language Process ing (NLP) and Geographic Information Systems (GIS) to analyze the spatial distribution and sentiment of restaurants based on TikTok data. Data was c ollected from TikTok using primary and secondary hashtags related to resta urant reviews in Bangkok. The resulting database enabled a detailed analys is of restaurant locations and customer sentiment using Logistic Regressio n for sentiment analysis. The findings indicate that negative reviews were predicted with the highest accuracy (84%)\, followed by positive (78%) an d neutral (76%) reviews. The spatial analysis identified a dense of restau rants in the inner districts of Bangkok. This integration of NLP and GIS n ot only mapped the popularity of restaurants as mentioned on TikTok but al so provided significant insights into consumer behavior and preferences. T he study demonstrates the effectiveness of combining NLP and GIS for geosp atial analysis\, offering a powerful tool for understanding social media t rends and their impact on local businesses. The results underscore the pot ential for leveraging social media data to inform urban planning and busin ess strategies\, particularly in the context of food and hospitality indus tries. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 2 SUMMARY:Social Media Data analysis in a Restaurant Context : A Case Study o f TikTok - Asamaporn Sitthi URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/TLBAPD/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-YMYRND@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T114500 DTEND;TZID=+07:20241217T120000 DESCRIPTION:Managing Geographic Information Systems (GIS) data with H3 (H3G eo) is an efficient and modern method for handling and analyzing geographi c data. H3 uses a hexagonal grid system that offers special features\, all owing data to be stored at resolution levels from 0 to 14\, which helps in dividing and storing data effectively. DTSTAMP:20260416T211403Z LOCATION:Room34-1104 SUMMARY:New Way Using H3 to Manage GIS Data - Tanaporn Songprayad\, Siriwim on Saotongthong\, Siriya Saenkhom-or URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/YMYRND/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-R3VX9M@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T114500 DTEND;TZID=+07:20241217T120000 DESCRIPTION:When considering the sustainability of open source as a future digital public good\, the cycle of success and contribution in the busines s sector becomes crucial.\n\n\nMIERUNE was born from the FOSS4G community in 2016 and has spent the past eight years solving client challenges as a location-based systems integrator\, growing steadily in the Japanese marke t.\nThrough these business activities\, MIERUNE not only actively gives ba ck to the FOSS4G community—both technically and financially—but also c reates local employment for GIS engineers\, helping to build a sustainable society.\n\n\nIn this presentation\, we will share specific examples of t he challenges we have faced to support the growth and development of FOSS4 G companies in the Asian region\, thereby contributing to the sustainabili ty of the whole community. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:How a FOSS4G-Born Business Grew in Japan: The Journey of MIERUNE - Yasuto FURUKAWA URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/R3VX9M/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-LTCJFE@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T130000 DTEND;TZID=+07:20241217T131500 DESCRIPTION:TBA DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Celebrating 20 Years of FOSS4G : The Journey of FOSS4G in Thailand - Sarawut ninsawat URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/LTCJFE/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-B3ULCK@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T131500 DTEND;TZID=+07:20241217T133000 DESCRIPTION:first ventured into the world of open-source GIS in 2003. Back then\, the landscape was much different—GRASS GIS and MapServer were kno wn names\, but familiar tools like QGIS and PostGIS were still in their in fancy\, with limited functionality and a small user base.\n\nIt was in thi s early era that Dr. Venkatesh Raghavan coined the term *FOSS4G*—a miles tone now celebrating its 20th anniversary since that pivotal moment in 200 4. Just a few months later\, in September\, the world’s first FOSS4G con ference was held at Chulalongkorn University in Bangkok\, where Europe’s GRASS GIS community met the MapServer community from North America. This historic gathering laid the groundwork for the creation of the OSGeo Found ation.\n\nIn this keynote\, I will take you down memory lane\, sharing the story of how FOSS4G was born and how it evolved into the vibrant ecosyste m we know today. Join me as I recount these foundational moments through s torytelling\, from a perspective shaped by those pioneering days. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Down the FOSS4G-Asia Memory Lane - Toru MORI URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/B3ULCK/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-KYNT8Z@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T133000 DTEND;TZID=+07:20241217T140000 DESCRIPTION:The GRASS GIS project\, a pioneering open-source geographic inf ormation system\, celebrated its 40th anniversary in 2023. As one of the l ong-standing contributors\, I am honored to reflect on the remarkable jour ney of this leading open-source geospatial software and community. Over th e past four decades\, GRASS GIS has grown from a modest project initiated by the U.S. Army Corps of Engineers to a robust\, globally recognized plat form for geospatial analysis and modeling. This evolution is a testament t o the dedication and collaborative spirit of the GRASS community\, which h as continually driven innovation and excellence.\n\nMy personal relationsh ip with GRASS GIS began over thirty years ago when I was a student and fir st encountered its powerful capabilities. Even then\, I was fascinated by its potential to revolutionize spatial analysis and environmental modeling . With the advent of the Internet\, we were able to build a passionate com munity behind the project. Through collaborative efforts\, we have signifi cantly expanded the functionality of GRASS GIS\, improved its user interfa ce through multiple iterations\, and ensured its adaptability to the ever- changing technological landscape.\n\nIn this keynote\, I will reflect on t he milestones that have shaped GRASS GIS from its inception at the U.S. Ar my Corps of Engineers' Construction Engineering Research Laboratory (USA/C ERL) to its current status as a cornerstone of the open-source geospatial ecosystem. The latest releases of GRASS GIS include thousands of changes\, including the new single-window GUI layout and enhanced parallelization c apabilities. These enhancements underscore our commitment to improving the user experience and computational efficiency. The past decade has also be en marked by vibrant community engagement through the OSGeo Foundation. I will highlight key contributions from the global community\, showcase grou ndbreaking research and applications\, touch on FOSS business models\, and explore the challenges we have overcome along the way.\n\nThe future of G RASS GIS is bright as we anticipate further innovation and expanded applic ations\, driven by the same collaborative ethos that has defined our past. Together we will continue to push the boundaries of what is possible in g eospatial analysis\, ensuring that GRASS GIS remains at the forefront of t his dynamic field. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:Celebrating four decades of innovation: The GRASS GIS Project - Mar kus Neteler URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/KYNT8Z/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-EHLQKJ@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T140000 DTEND;TZID=+07:20241217T143000 DESCRIPTION:Water does not flow according to geographical boundaries but it follows\nelevation. Inland waters from rivers and lakes present crucial n atural\nresources playing an indispensable role in the global hydrological cycle.\nStill\, their conventional monitoring is constrained by poor spat ial\ncoverage. Though satellites help improve coverage\, the hydraulic pro perties\nof rivers often change at a rate faster than the temporal samplin g of\nsatellites. Through this talk\, two novel web applications are intro duced\nfor rivers and lakes built using geospatial datasets and python. Wh ile the\nfirst shall seamlessly extract time series of water surface area for\nrivers\, lakes and reservoirs from Sentinel-1 VV polarized SAR data. The\nsecond application shall integrate dynamic lake water extents for imp roving\nlake water surface temperatures\, thereby challenging conventional norms. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:LEVERAGING GEOSPATIAL DATA FOR TRACKING WATER FROM SPACE USING PYTH ON PROGRAMMING - J. Indu URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/EHLQKJ/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-asia-2024-ZLEDP3@talks.geoinfo-lab.org DTSTART;TZID=+07:20241217T150000 DTEND;TZID=+07:20241217T153000 DESCRIPTION:Enhancing the efficiency of the Electricity Generating Authorit y of Thailand (EGAT)’s transmission system through satellite imagery and LiDAR. With over 470 transmission lines spanning 25\,000 km\, expansion p lans aim to provide nationwide coverage. LiDAR is used for aerial mapping to create an accurate initial database\, while CCTV systems enable real-ti me surveillance. Satellite imagery\, particularly SAR satellite imagery co mbined with InSAR and machine learning\, ensures precise monitoring of enc roachments. Integrated GIS application support right-of-way management\, c ontributing to safe and efficient power distribution. DTSTAMP:20260416T211403Z LOCATION:Auditorium Hall 1 SUMMARY:POWERING THE FUTURE GRID : INTELLIGENCE TRANSMISSION MONITORING WIT H SAR SATELLITE IMAGERY AND LiDAR - Kamolratn Chureesampant URL:https://talks.geoinfo-lab.org/foss4g-asia-2024/talk/ZLEDP3/ END:VEVENT END:VCALENDAR