arXiv:2609.36315v1 Announce Type: cross
Abstract: Wildfires are an increasing hazard to ecosystems, air quality, and human systems, creating a growing need for datasets that support systematic develo...
By Arya Kondur, Giosue Migliorini, Cameron Schmitt, Francesco Immorlano, Tairan Wang, Rebecca C. Scholten, Efi Foufoula-Georgiou, Gary Johnson, Chris Lautenberger, Valentin Waeselynck, J. Shane Romsos, Kasra Shamsaei, Alejandro Tejedor, Tianjia Liu, Yang Chen, Padhraic Smyth, James T. Randerson
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.
By Matheus F. Kovaleski, Cristiano Premebida, Jo\~ao Ruivo Paulo
arXiv:2607. 16472v1 Announce Type: cross Abstract: Over the past decades, the frequency of global wildfires has been increasing steadily.
By Yugong Zeng, Jonathan Wu
Annual Earth‑observation embeddings, specifically Tessera, can encode wildfire disturbance signals well enough to map burned areas without needing curated fire‑specific imagery or dense time‑series analysis. In tests, linear models using a single Tessera embedding matched or outperformed paired pre‑ and post‑fire HLS imagery and post‑fire imagery alone, achieving high F1 scores for burn‑scar delineation and regional mapping. The approach successfully mapped all same‑year fires in benchmark scenes, recovered 97% of California burned area without California training data, transferred to European fires with high accuracy, and even estimated ignition timing within a 13‑day error margin.
"whyItMatters":"The study demonstrates that pre‑trained annual embeddings can simplify and scale burned‑area mapping, reducing reliance on dense time‑series data and enabling more efficient wildfire monitoring."
By Jovana Knezevic, Clement Atzberger, Zhengpeng Feng, Adam F. A. Pellegrini, Srinivasan Keshav, David Coomes
The paper presents a three‑stage framework that merges a Random Forest (RF) model with a cellular automaton (CA) to model large‑scale wildfire spread. First, an RF trained on the 2021 Canadian fire season predicts daily pixel‑level fire‑occurrence probabilities. Second, optional spread‑rate priors are supplied by quantile gradient boosting models for sensitivity analysis. Third, an RF‑informed CA integrates the RF probability layer with neighbourhood‑driven spread on a 5 km grid, achieving higher spatial overlap than CA‑only baselines in 2023 simulations and demonstrating improved performance on 2022–2024 datasets (AUC 0.725–0.795).
By Siyu Chen, Esha Saha, Hao Wang
arXiv:2606. 11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread.
By Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala, Takuya Kurihana, Hon Yung Wonga