mlsanityData to 13 Oct 2026

CaBuAr: California Burned Areas dataset for delineation

arXiv 2401.11519 ↗First posted 21 Jan 2024cs.CV cs.LG eess.IVcited by 9

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Abstract (arXiv)

Forest wildfires represent one of the catastrophic events that, over the last decades, caused huge environmental and humanitarian damages. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools. This paper introduces a novel open dataset that tackles the burned area delineation problem, a binary segmentation problem applied to satellite imagery. The presented resource consists of pre- and post-fire Sentinel-2 L2A acquisitions of California forest fires that took place starting in 2015. Raster annotations were generated from the data released by California's Department of Forestry and Fire Protection. Moreover, in conjunction with the dataset, we release three different baselines based on spectral indexes analyses, SegFormer, and U-Net models.

Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Builds on 9 of its 38 references that are in the map. First posted on arXiv 21 Jan 2024.

Builds on

9 of 38 references.

References · 9

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  3. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,930
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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.

  1. 2024A Change Detection Reality Checkcited by 13
  2. 2024CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagerycited by 9
  3. 2024QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1cited by 8
  4. 2025Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineationcited by 0
  5. 2025HydroChronos: Forecasting Decades of Surface Water Changecited by 0
  6. 2025A Novel Dataset for Flood Detection Robust to Seasonal Changes in Satellite Imagerycited by 0
  7. 2026LDGuid: A Framework for Robust Change Detection via Latent Difference Guidancecited by 0
  8. 2026QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagerycited by 0
  9. 2026Temporal Modelling for Burn Scars on Sentinel-3cited by 0