# CaBuAr: California Burned Areas dataset for delineation

arXiv: https://arxiv.org/abs/2401.11519 · HTML: https://mlsanity.com/paper/2401.11519/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [When Does Contrastive Visual Representation Learning Work?](https://mlsanity.com/paper/2105.05837/) (2021)
- [SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning](https://mlsanity.com/paper/2111.04724/) (2021)
- [Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data](https://mlsanity.com/paper/2112.02447/) (2021)
- [A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning](https://mlsanity.com/paper/2204.00951/) (2022)

## Built on by (most cited first, 9 of 9)

- [A Change Detection Reality Check](https://mlsanity.com/paper/2402.06994/) (2024)
- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1](https://mlsanity.com/paper/2403.18116/) (2024)
- [Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation](https://mlsanity.com/paper/2504.19589/) (2025)
- [HydroChronos: Forecasting Decades of Surface Water Change](https://mlsanity.com/paper/2506.14362/) (2025)
- [A Novel Dataset for Flood Detection Robust to Seasonal Changes in Satellite Imagery](https://mlsanity.com/paper/2507.23193/) (2025)
- [LDGuid: A Framework for Robust Change Detection via Latent Difference Guidance](https://mlsanity.com/paper/2605.15582/) (2026)
- [QFireNet: A Quantum-Enhanced U-Net for Wildfire Segmentation from Sentinel-2 Imagery](https://mlsanity.com/paper/2607.14160/) (2026)
- [Temporal Modelling for Burn Scars on Sentinel-3](https://mlsanity.com/paper/2609.34596/) (2026)

## 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.