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arXiv Machine Learning By Shreyan Mitra, Mohammadreza Narimani, Parastoo Farajpoor

A Sentinel-2 benchmark dataset for deep-learning active-fire segmentation across 25 California wildfires

Read the original on arXiv Machine Learning →

The article introduces an open image dataset for active‑fire segmentation in satellite imagery, comprising 2,148 image‑mask pairs from 25 California wildfires captured between July 2020 and August 2026. Each 512×512 pixel, three‑channel image is a Sentinel‑2 Level‑2A composite of bands B12, B11, and B8A, with a fixed linear rendering applied uniformly. Masks distinguish background, short‑wave‑infrared rule‑based active fire, and invalid observations, and the dataset includes chip‑level metadata, an incident‑disjoint split, and a mask‑blind analyst review of 233 test chips.

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