Synthetic Aperture Radar (SAR) remote sensing provides powerful capabilities for monitoring Earth’s surface under all weather conditions. However, its data complexity and interpretation challenges often hinder large scale operational use. This workshop introduces an open-source framework for AI- Guided SAR Remote Sensing, demonstrating how Artificial Intelligence and Large Language Models (LLMs) can accelerate SAR data processing, enhance feature detection, and finding application in other sectors like agriculture, forest and disaster risk reduction.
Participants will learn to integrate Python-based tools such as GDAL, Rasterio, geopandas, Numpy, and Pytorch to automate SAR workflows for applications like flood mapping, landslide monitoring, and deformation analysis. Emphasizing both theory and practical implementation, the workshop empowers participants to build intelligent, reproducible pipelines that combine open geospatial software and machine learning for next-generation SAR analytics
The workshop will get started with introduction of Remote sensing (optical and Microwave) and then properties of SAR data, preprocessing the SAR data (multiloading, image co-registration and filtering and geocoding the SAR data). And the we directly jump into python and DL. After that with our background on SAR, we will generate the result by taking python code from LLM (chat gpt and deepseek) .
By the end of the session, participants will:
• Understand how AI and LLMs can be used to generate Python code for SAR data processing.
• Gain hands-on experience in reading and visualizing SAR imagery and apply metadata to preprocess and further process to generate the result from SAR data.
• Build and test a simple AI-driven SAR feature extraction workflow.
• Access ready-to-use Python notebooks and an open GitHub repository for further experimentation.
• Learn reproducible, scalable methods for integrating AI with open geospatial technologies.