GeoAI Dataset for Urban Water Body Detection Using TerraSAR-X Satellite Radar Imagery

This study presents the generation of a GeoAI dataset for urban water body detection using TerraSAR-X satellite synthetic aperture radar (SAR) imagery. The study area includes urban regions in Seoul and Gyeonggi Province, chosen for their complex structures and frequent flooding, which pose challenges for SAR analysis. The data preprocessing involved generating Sigma0 images, image co-registration, median filtering for speckle noise reduction, decibel conversion, and orthorectification using Copernicus DEM for precise geometric correction. Label data were created using the global river widths from Landsat dataset combined with the Otsu thresholding method and fine-tuned with Google Map imagery. Annotation guidelines were meticulously designed to account for SAR-specific phenomena such as layover, corner reflections, and side lobe effects, ensuring consistent and accurate labeling across different orbits and observation conditions. The resulting dataset supports deep learning models in learning geometric characteristics of SAR imagery, enhancing water body detection capabilities. This work provides a foundational resource for future applications in urban water management and climate-resilient disaster response.