arXiv Machine Learning September 15, 2026 By Ji Lu, Huiran Duan, Bo Zhao, Xianglong Wang, Yiru Fang, Kuo Yang, Xiaoqin Feng, Jianping Gou

Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models

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arXiv Machine Learning

Jul 10

Scalable and Trustworthy Earth Observation Foundation Models

arXiv:2607. 07758v1 Announce Type: new Abstract: Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks.

By Syed Usama Imtiaz, Mitra Nasr Azadani, Nasrin Alamdari

reinforcement-learning multimodal benchmarks

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

Aug 20

Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling

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 AI

Jun 26

EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting

arXiv:2606. 27277v1 Announce Type: new Abstract: Earth Observation (EO) forecasting aims to predict future Earth surface dynamics from satellite observations under changing meteorological conditions.

By Junwei Luo, Shuai Yuan, Zhenya Yang, Yansheng Li, Zhe Liu, Hengshuang Zhao

llms diffusion benchmarks

arXiv Machine Learning

Aug 26

Data Leakage Inflates Generalizability of Power Outage Prediction Models

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

rag

arXiv Machine Learning

Aug 4

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams

arXiv:2608. 00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated.

By Fengxiang Wang, Qiuyang Yu, Yueying Li, Mingshuo Chen, Chengchi Fei, Kaiyi Xu, Lixin Gu, Wangxu Wei, Junchao Gong, Lipeng Ma, Jiong Wang, Fenghua Ling, Wenlong Zhang, Xue Yang, Wenjing Yang, Ben Fei, Long Lan

llms multimodal benchmarks

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Related stories

Scalable and Trustworthy Earth Observation Foundation Models

Uncertainty-Aware Deep Learning for Wildfire Danger Forecasting

Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling

EO-WM: A Physically Informed World Model for Probabilistic Earth Observation Forecasting

Data Leakage Inflates Generalizability of Power Outage Prediction Models

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams