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
arXiv:2608. 07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions.
By Nicolas Caron, Christophe Guyeux, Hassan Noura, Benjamin Aynes
arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.
By Quinn Ledingham, Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Saeid Taleghanidoozdoozan, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu
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
The paper presents modular deep learning augmentations for next‑day wildfire spread prediction, including wind‑ and slope‑conditioned attention biases, physics‑feature retrieval‑augmented output correction, and fire‑conditioned dual‑stream gating. These modules are evaluated on five backbone models using the Next Day Wildfire Spread benchmark, with staged ablations, directional audits, retrieval perturbations, calibration measures, and computational comparisons. The best augmented SwinUNETR model achieves an F1 score of 0.4216 and an AUC‑PR of 0.3673, while a mixed ensemble reaches 0.4292 and 0.3790, demonstrating that predictive performance, operational trustworthiness, and computational practicality can be simultaneously improved.
By Miguel Esparza, Aydin Ayanzadeh Ahmad Mousavi, Ali Mostafavi
A deep learning model (U‑Net‑ID) mapped 6.5 million large trees (crown area ≥ 100 m²) across 78.7 % of the Sierra Nevada Floristic Province using sub‑meter aerial imagery from 2020. The spatial distribution of these trees correlated with elevation, temperature, and precipitation. From 2020 to 2025, Sentinel‑2 time series and BFAST breakpoint analysis revealed that wildfires were the dominant driver of large‑tree mortality, killing 10 % of all large trees, especially during the extreme 2020‑2021 fire seasons.
By Fabien H. Wagner, Dan J. Dixon, Christopher W. Woodall, Mayumi C. M. Hirye, Felipe Saad, Griffin Carter, Ricardo Dalagnol, Lorena Alves, Cynthia Creze, Stephen C. Hagen, Zhihua Liu, Christopher Mihiar, Adugna Mullissa, Le Bienfaiteur Sagang Takougoum, Bryan Shaddy, Yan Yang, Dafeng Zhang, Sassan Saatchi
The study evaluates deep learning surrogates for wildfire spread prediction, training four architectures on 10,584 high‑resolution simulations from Catalonia. Results show that only surface fuel load significantly predicts burn probability, and convolutional models mainly use distance to the fire front while a transformer model emphasizes fuel and terrain. When applied to a new region without retraining, the models still perform reasonably, with only a modest accuracy drop.
By Marcin Lawenda, Aleksandra Krasicka, David Caballero, Luis Torres, {\L}ukasz Szustak
arXiv:2608. 12663v1 Announce Type: cross Abstract: Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products.
By Yuan Zhuang, Sanaa Hobeichi, Peng Shi, Fei Huang
arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.
By Luke J. Zachmann, David D. Diaz, Vincent A. Landau, Chelsey Walden-Schreiner, Tony Chang, Nathan E. Rutenbeck, Katharyn A. Duffy, Kiarie Ndegwa, Andreas Gros, Scott Conway, Guy Bayes
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:2607. 07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.
By Yongcan Huang, Li Jiang, Ze Yu Liu
arXiv:2509. 25017v2 Announce Type: replace Abstract: Wildfires are among the most severe natural hazards, posing a significant threat to both humans and natural ecosystems.
By Spyros Kondylatos, Nikolas Papadopoulos, Gustau Camps-Valls, Ioannis Papoutsis