arXiv Machine Learning August 4, 2026 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

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 6

DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

arXiv:2606. 06217v1 Announce Type: cross Abstract: When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, often from noisy low-altitude UAV views and under tight on-site compute constraints.

By Tan Zhang, Quanyou Li, Lu Zhang, Jun Liu, Xiaofeng Zhu, Ping Hu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

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 AI

Jul 22

Now We Know? A Systematic Comparison of TerraMind and THOR

arXiv:2607. 18504v1 Announce Type: cross Abstract: Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, how much is decoder capacity, and how much is a use-case-specific artefact?

By Frederick Schindlegger, Kenzo Bounegta, Eva Gmelich Meijling, Johannes Jakubik, Arnt-B{\o}rre Salberg, Theodor Forgaard, Nicolas Longepe, Valerio Marsocci

computer-vision fine-tuning multimodal benchmarks

arXiv AI

Aug 3

Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping

arXiv:2604. 26051v2 Announce Type: replace-cross Abstract: The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monitoring.

By Hyunho Lee, Wenwen Li

safety

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 AI

Jul 7

A harmonised dataset for Earth system foundation models

arXiv:2607. 03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change.

By Carlos Rodriguez-Pardo, Massimo Tavoni

multimodal

Related stories

DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

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

Now We Know? A Systematic Comparison of TerraMind and THOR

Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping

Scalable and Trustworthy Earth Observation Foundation Models

A harmonised dataset for Earth system foundation models