Floods are among the most destructive extreme events that exist, being the main cause of people affected by natural disasters. In the near future, estimated flood intensity and frequency are projected to increase. In this context, automatic and accurate satellite-derived flood maps are key for fast emergency response and damage assessment. However, current approaches for operational flood mapping present limitations due to cloud coverage on acquired satellite images, the accuracy of flood detection, and the generalization of methods across different geographies. In this work, a machine learning framework for operational flood mapping from optical satellite images addressing these problems is presented. It is based on a clouds-aware segmentation model trained in an extended version of the WorldFloods dataset. The model produces accurate and fast water segmentation masks even in areas covered by semitransparent clouds, increasing the coverage for emergency response scenarios. The proposed approach can be applied to both Sentinel-2 and Landsat 8/9 data, which enables a much higher revisit of the damaged region, also key for operational purposes. Detection accuracy and generalization of proposed model is carefully evaluated in a novel global dataset composed of manually labeled flood maps. We provide evidence of better performance than current operational methods based on thresholding spectral indices. Moreover, we demonstrate the applicability of our pipeline to map recent large flood events that occurred in Pakistan, between June and September 2022, and in Australia, between February and April 2022. Finally, the high-resolution (10-30m) flood extent maps are intersected with other high-resolution layers of cropland, building delineations, and population density. Using this workflow, we estimated that approximately 10 million people were affected and 700k buildings and 25,000 km[Formula: see text] of cropland were flooded in 2022 Pakistan floods.
© 2023. The Author(s).