arXiv Machine Learning

Evaluating AlphaEarth Foundations Embeddings for Wildfire Susceptibility Mapping

arXiv:2608. 12663v1 Announce Type: cross Abstract: Wildfire susceptibility mapping typically relies on physical variables assembled from multiple remote-sensing, climate, and geospatial products.

arXiv Machine Learning

Jun 10

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

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

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

computer-vision

arXiv Machine Learning

Jul 10

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

arXiv:2607. 07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.

By Yongcan Huang, Li Jiang, Ze Yu Liu

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 20

DELUGE: Towards Continental-Scale Daily Pluvial Flood Damage Prediction via Interpretable Conditioning on Foundation Model Embeddings

arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.

By Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma, Tom Corringham

rag multimodal safety

arXiv Machine Learning

5d ago

Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods

arXiv:2608. 12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress.

By Matthew Kahn, Milan Arjel, Nirmala Adhikari, Mingmar Sherpa, James Pope

arXiv AI

Jun 9

Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events

arXiv:2606. 07780v1 Announce Type: new Abstract: Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response.

By Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane, Caden Helbling, Deepak Shah, Othneil Drew, Iksha Gurung, Manil Maskey, Rahul Ramachandran

arXiv Machine Learning

Jul 31

Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

arXiv:2607. 27217v1 Announce Type: cross Abstract: Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements.

By Shashika Lamahewage, Chandi Witharana

rag multimodal safety

arXiv Machine Learning

Aug 11

Spatial Heterogeneity-Aware Multi-Hazard Susceptibility and Risk Mapping at Regional Scale

arXiv:2608. 08321v1 Announce Type: new Abstract: Floods and landslides often co-occur, but their relationships with environmental controls vary spatially.

By Aswathi Mundayatt, Siddharth Anil, Hitanshu Seth, Jaya Sreevalsan-Nair

arXiv AI

Jul 2

Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings

arXiv:2601. 01558v2 Announce Type: replace-cross Abstract: Predicting river flow in places without streamflow records is challenging because basins respond differently to climate, terrain, vegetation, and soils.

By Pengfei Qu, Wenyu Ouyang, Chi Zhang, Yikai Chai, Shuolong Xu, Lei Ye, Yongri Piao, Miao Zhang, Huchuan Lu

rag

arXiv Machine Learning

Jul 7

Probing Geospatial SSL Representations with Environmental Signals

arXiv:2607. 05207v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task.

By Rohita Mocharla, Vishal M. Patel

benchmarks

arXiv Machine Learning

Aug 7

Evaluating Machine Learning Models for Post-Wildfire Debris-Flow Prediction

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

benchmarks safety

arXiv Machine Learning

Jun 19

Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

arXiv:2606. 20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this.

By Htet Yamin Ko Ko, Clement Atzberger

rag data-engineering

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