Open-Earth-Monitor Global Workshop 2026

Flash flood events are increasing in frequency and intensity in Mediterranean regions, requiring rapid, reliable, and scalable monitoring approaches to support emergency response and climate adaptation. Earth Observation (EO) offers a powerful means to provide timely spatial intelligence; however, single-sensor approaches remain limited by cloud cover, revisit frequency, and data latency. This work presents an automatic, multi-sensor, modular, and open-source flood mapping framework designed to deliver actionable information for emergency responders through near-real-time flood detection coupled with a first-pass impact assessment.
The proposed methodology integrates Synthetic Aperture Radar (Sentinel-1) and multispectral imagery (Sentinel-2 and Landsat 8) with ancillary geospatial datasets within a unified processing pipeline. A change detection approach is applied to pre- and post-event observations, followed by automated thresholding and morphological filtering to generate consistent flood extent maps. To reduce noise sensitivity, outputs from multiple sensors are then fused at the pixel level to generate flood extent, severity, and damage assessment maps.
The framework was validated against ground-truth data from the October 2024 flash flood event in Valencia, with results clearly demonstrating the value of automated multi-sensor data fusion by increasing the likelihood of acquiring usable observations by up to ~60%. This modular architecture is fully reproducible and designed for extensibility, enabling the integration of additional sensors and seamless deployment within open EO ecosystems and distributed data infrastructures.