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Multi-Hazard Mapping using Earth Observation data and Multi-Task Learning
This dataset accompanies the study "Multi-Hazard Mapping using Earth Observation data and Multi-Task Learning". The dataset provides examples of harmonized reference data combining water body and burned area datasets for training and evaluating multi-task deep learning models b
File Size83.8 MB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views91
Total Downloads23
This dataset accompanies the study “Multi-Hazard Mapping using Earth Observation data and Multi-Task Learning”.
The dataset provides examples of harmonized reference data combining water body and burned area datasets for training and evaluating multi-task deep learning models based on Sentinel-2 and Landsat-8 imagery.
Contents
- ‘experiments/` – folder containing example experiments conducted within the study
- `experiments/mtl/` – experiments conducted in multi-task setup
- `experiments/stl/` – experiments conducted in single-task setup
- `reference_data` – example reference patches
Source Data
All source data used in this study are publicly available:
- Global S1S2-Water Benchmark Dataset from Wieland et al. (2024) https://zenodo.org/records/11278238
- Burned Area Reference Database (BARD) from Copernicus Climate Change Service https://edatos.consorciomadrono.es/dataset.xhtml?persistentId=doi:10.21950/BBQQU7
- Copernicus Emergency Management Service (EMS) rapid mapping products https://zenodo.org/records/6597139
- HLS burn scars dataset https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars
- Sentinel-2 and Landsat-8 Collection 2 Level-2 Surface Reflectance via Microsoft Planetary Computer https://planetarycomputer.microsoft.com/
License
Creative Commons Attribution 4.0 International (CC BY 4.0)
Citation
Please cite the associated publication and this dataset when using the data.
[Publication reference to be added upon acceptance]
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Files are hosted on the source repository. Click download to access the full dataset.
Orynbaikyzy, Aiym (2026). Multi-Hazard Mapping using Earth Observation data and Multi-Task Learning. https://doi.org/10.5281/zenodo.19879172