arXiv:2512. 07925v4 Announce Type: replace-cross Abstract: Ongoing armed conflict in Sudan highlights the need for rapid monitoring of conflict-related fire-affected areas.
By Kuldip Singh Atwal, Dieter Pfoser, Daniel Rothbart
arXiv:2609.18227v1 Announce Type: new
Abstract: Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems m...
By Sha Lu, Yu Sun, Liang Zhao, Jixue Liu, Lin Liu, Jiuyong Li, A. K. Qin, Alejandro Mousist, Stefan Peters
Wildfire smoke detection from satellite imagery is critical for early warning and rapid response. For onboard satellite deployment, detection systems must operate under strict memory and latency const...
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 introduces an AI surrogate modelling framework that accelerates wildfire hazard mapping across Canada. By training a multi‑task deep learning model on national‑scale data, the surrogate reproduces key outputs of the computationally intensive BurnP3+ simulator—burn probability, mean fire intensity, and mean rate of spread—in seconds instead of days or weeks. The framework includes standardized training, fine‑tuning, and evaluation protocols, domain‑specific metrics, and counterfactual evaluation to ensure robust out‑of‑distribution performance, achieving up to 806‑fold CPU speed‑up and 13% memory usage compared to the original simulator.
By Hager Radi Abdelwahed, Toluwaleke Olutayo, Chirag Goel, Jana Osea, Aldo Zaimi, Tom Swystun, Sandy Erni, Francisco Emiliano Lopez Saavedra, Yan Boulanger, Ga\'etan Marceau Caron
arXiv:2609.09257v1 Announce Type: cross
Abstract: Sensor-based AI systems are rarely operated under the conditions under which they were trained: devices, personnel and recording epochs change, and e...
By Benny Platte (Mittweida University of Applied Sciences), Rico Thomanek (Mittweida University of Applied Sciences), Christian Roschke (Mittweida University of Applied Sciences), Marc Ritter (Mittweida University of Applied Sciences)