A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires, July 2020–August 2026
Year: 2026
Type: data-paper
Abstract: This article describes an open image dataset for active-fire segmentation in satellite imagery. It contains 2148 image-mask pairs from 25 California wildfires, acquired from 23 July 2020 to 21 August 2026. Each image is a 512 × 512-pixel, three-channel composite of Sentinel-2 Level-2A bands B12, B11 and B8A at 20 m sampling. The same linear rendering is applied to every image. Masks distinguish background, SWIR-rule active fire and invalid observations. They were generated from shortwave-infrared brightness and near-infrared contrast, followed by two steps of neighborhood growth. The release includes chip-level metadata and a partition with 18 training, three validation and four test wildfires. Each wildfire belongs to only one partition. Of the image pairs, 841 contain active-fire labels, which occupy 0.0766% of all grid cells. An analyst reviewed 233 test chips without seeing the rule masks. These annotations provide a separate assessment of label agreement at chip and connected-component levels. The accompanying code includes a ResNet-34 U-Net with checkpoint and probability-threshold selection on validation data. The images and masks provide a fixed input for comparing segmentation methods on wildfires excluded from training. Dataset version 1.0.0 is deposited on Zenodo; the preparation and training software is available on GitHub.
Source: Data in Brief
Institution: University of California, Davis
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,220
OpenAlex ID: W7221974265