The compendium compiles practical case studies on the use of Geospatial Artificial Intelligence (GeoAI) to enhance disaster risk reduction and emergency response across diverse geographic and institutional contexts. The compendium features selected case studies submitted by twenty-seven Regional Su ... pport Offices (RSOs) working across Asia, Africa, Latin America, and Europe. These examples highlight how GeoAI, is being used to forecast floods, map wildfire risk, assess landslide susceptibility, monitor droughts, and support emergency response. Each project demonstrates how cloud-based platforms and machine learning tools help governments act faster and more precisely when disaster strike. more

Flood mapping in urban areas poses significant challenges for Synthetic Aperture Radar (SAR) sensors due to limitations in detecting water in regions characterized by dense vegetation, urban infrastructure, or complex surface conditions. These limitations include reduced sensitivity in vegetated or ... built-up areas and water-like backscatter effects on smooth, dry, or snow-covered surfaces. The Global Flood Monitoring (GFM) platform employs Sentinel-1 SAR backscatter data for automated flood delineation but recognizes the constraints posed by such conditions. To address these challenges, the GFM has integrated an exclusion mask that highlights regions prone to SAR-based misclassification. more