arXiv:2607. 16472v1 Announce Type: cross Abstract: Over the past decades, the frequency of global wildfires has been increasing steadily.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper proposes a data-driven protocol that uses multispectral Landsat‑8 imagery and connected‑component analysis to characterize fire‑region size distributions for active wildfire segmentation. Three segmentation architectures—U‑Net, DeepLabV3+, and SegFormer—are evaluated under different SWIR‑based spectral configurations, with U‑Net showing the strongest robustness and SWIR2 consistently delivering the best results. The study highlights the importance of both spectral band selection and architectural design for robust satellite‑based active wildfire mapping, especially when training on low fire‑pixel density images.
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.
The paper examines RGB‑infrared fusion for binary wildfire segmentation using UAV imagery on the FLAME3 dataset. It compares RGB and infrared baselines with three fusion strategies across U‑Net, DeepLabV3+, and SegFormer architectures. Results show thermal data dominates segmentation performance, and feature‑level fusion with transformer‑based models yields the best results.
ShearFuse-UNet is a lightweight deep learning model designed for next‑day wildfire spread prediction using multi‑modal satellite data. It incorporates three transform‑domain branches—Fast Walsh‑Hadamard Transform, Discrete Cosine Transform, and a cone‑adapted digital Shearlet residual—within each encoder block of a U‑Net backbone, fusing spectral representations via a learned SpectralFusion gate and adding Shearlet reconstruction as a residual. On the WildfireSpreadTS dataset, the model achieves an F1 score of 0.596 with only 267k parameters, outperforming a larger ResNet18‑based U‑Net and demonstrating a favorable accuracy‑efficiency trade‑off, with additional validation on the Google Next‑Day Wildfire Spread dataset.
arXiv:2606.10174v2 Announce Type: replace Abstract: Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we i...
arXiv:2608. 07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions.