Land Use Land Cover Classification Automation Development using Free and Open-Source Software
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 classification through the integration of artificial intelligence (AI) models.
The proposed system will consist of two main components: (1) an automated satellite data fetching and preprocessing module, and (2) an AI-driven LULC classification module. The first component will leverage open-source tools to access and prepare satellite imagery from freely available sources, such as Landsat and Sentinel missions. This module will handle tasks including data download, atmospheric correction, cloud masking, and image compositing.
The second component will employ state-of-the-art machine learning algorithms, particularly deep learning models, to perform seasonal LULC classification. The system will be trained on diverse datasets to recognize and categorize various land cover types across different seasons, accounting for temporal variations in vegetation, urban expansion, and other dynamic landscape features.
By automating these processes, the proposed system aims to significantly reduce the time and expertise required for LULC analysis, making it more accessible to researchers, urban planners, and environmental managers. The use of free and open-source software ensures that the developed tools will be widely available and customizable for different geographical contexts and research needs.
This contributes to the advancement of remote sensing applications and supports informed decision-making in land management, urban planning, and environmental conservation efforts.