arXiv:2609.16168v2 Announce Type: replace-cross
Abstract: Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive...
By Parastoo Farajpoor, Mahla Ardebili Pour, Mohammad Bagher Ghiasi, Mohammadreza Narimani
Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems. We ex...
The study presents a GeoAI workflow that links 12,081 CAL FIRE damage inspections from the January 2025 Palisades Fire to pre‑fire satellite and GIS data, achieving an ROC‑AUC of 0.92 with cross‑validation but dropping to 0.75 under spatial block validation. Building density within 100 m emerged as the strongest predictor of residential structure loss, while vegetation moisture and greenness had opposing effects at different distances. The findings emphasize the importance of neighborhood‑scale susceptibility screening, moisture‑aware vegetation management, and spatial block validation for urban wildfire modeling.
By Parastoo Farajpoor, Mohammadreza Narimani
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
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
arXiv:2608.13856v2 Announce Type: replace-cross
Abstract: Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optic...
By Mohammadreza Narimani, Shreyan Mitra, Parastoo Farajpoor