Description
10 m spatial resolution flood and water masks that can be used to develop machine learning models to classify flooding in satellite images.
The flood and water masks are generated by combining various datasets including the ESA WorldCover 10m v100 and vector flood and water layers from the Copernicus Emergency Management System (EMS) Rapid Mapping Activation events.
In total, 513 flood and water mask layers were generated as GeoTIFF files with a 10 m spatial resolution. Each of these layers corresponds to a flood event and date (there can be many flood mask layers per-event which represent flooding on different days as the event evolves). Flood and water masks were generated for 65 flood events since 2018 in 26 countries spanning the tropics and sub-tropics. This broad scale dataset can be used to train flood detection models, that can detect and segment flooding in satellite images, and be fine-tuned for Pacific geographic contexts.
The flood and water masks are generated by combining various datasets including the ESA WorldCover 10m v100 and vector flood and water layers from the Copernicus Emergency Management System (EMS) Rapid Mapping Activation events.
In total, 513 flood and water mask layers were generated as GeoTIFF files with a 10 m spatial resolution. Each of these layers corresponds to a flood event and date (there can be many flood mask layers per-event which represent flooding on different days as the event evolves). Flood and water masks were generated for 65 flood events since 2018 in 26 countries spanning the tropics and sub-tropics. This broad scale dataset can be used to train flood detection models, that can detect and segment flooding in satellite images, and be fine-tuned for Pacific geographic contexts.
Date made available | 16 Aug 2023 |
---|---|
Publisher | The University of Western Australia |
Date of data production | 16 Aug 2023 |
Keywords
- climate
- flood
- machine learning
- remote sensing