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: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
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
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
The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.
By Artur Sokolovsky, Bhavik Merai, Moe Jafari, Muen Chen
arXiv:2608.24665v1 Announce Type: new
Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...
By Yamil Essus, Ranga Raju Vatsavai, Benjamin Rachunok