Datasets › Burned Area Delineation from Satellite Imagery

Burned Area Delineation from Satellite Imagery (A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery)

archive 2025-07-28

The dataset contains 73 satellite images of different forests damaged by wildfires across Europe with a resolution of up to 10m per pixel. Data were collected from the Sentinel-2 L2A satellite mission and the target labels were generated from the Copernicus Emergency Management Service (EMS) annotations, with five different severity levels, ranging from undamaged to completely destroyed.

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
severity prediction Burned Area Delineation from Satellite Imagery DS-UNet RMSE 1.30 Attention to Fires: Multi-Channel Deep Learning Models... dbdmg/rescue 1 Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 1. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction 1 1 22 Nov 2021 not harvested

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • Burned Area Delineation from Satellite Imagery

1 variant name, as the archive lists them.

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