arXiv Computer Vision By Mohammadreza Narimani, Shreyan Mitra, Parastoo Farajpoor

From crown candidates to neighborhood screening: integrating optical GeoAI and spatial modeling for urban-canopy assessment in Davis, California

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arXiv Machine Learning
Jun 26

Self-Supervised Tree-level Biomass Estimation in Urban Environments From Airborne LiDAR and Optical Observations

arXiv:2606. 26194v1 Announce Type: cross Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse products that cannot resolve individual crowns or fine-scale heterogeneity.

By Jose Bermudez (McMaster University, Hamilton, Ontario, Canada), Zilong Zhong (McMaster University, Hamilton, Ontario, Canada), Dominic Cyr (, Environment and Climate Change Canada, Montreal, Quebec, Canada), Camile Sothe (Planet Labs PBC, San Francisco, California, USA), Alemu Gonsamo (McMaster University, Hamilton, Ontario, Canada)
arXiv Machine Learning
Jun 19

Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

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
arXiv Computer Vision
Sep 17

Rapid Loss of the Sierra Nevada's Largest Trees Driven by Fire

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 Machine Learning
Oct 28

Explainable GeoAI for urban wildfire structure loss: vegetation and built environment in the 2025 Palisades Fire

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
arXiv AI
Sep 17

A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products

The paper evaluates the Quebec-specific 10‑m land‑cover product COTQ against three global 10‑m datasets (ESA WorldCover, ESRI LandCover, and Google DynamicWorld). Using structural indicators, spectral separability metrics, and photo‑interpretation, the study finds that COTQ most closely resembles ESA WorldCover but shows systematic differences in urban, wetland, and rocky classes. The analysis clarifies COTQ’s relative strengths and weaknesses for operational land monitoring in Quebec.

By \'Etienne Clabaut, Samuel Foucher, Yacine Bouroubi