arXiv AI

Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning

arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.

arXiv AI

Jun 17

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.

By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni

safety

arXiv AI

Jun 10

Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset

arXiv:2606. 10329v1 Announce Type: cross Abstract: As one of the most destructive natural disasters, earthquakes have struck many countries around the world in recent years, causing serious economic losses.

By Yunlong Liu, Zekai Zhang

benchmarks

arXiv AI

Sep 10

MCANet: A Multi-Scale Class-Specific Attention Network for Multi-Label Post-Hurricane Damage Assessment Using UAV Imagery

arXiv:2509.04757v2 Announce Type: replace-cross Abstract: Hurricanes cause widespread damage to buildings, roads, and other infrastructure, making timely post-disaster damage assessment critical for...

By Zhangding Liu, Neda Mohammadi, John E. Taylor

llms computer-vision

arXiv AI

Jul 7

SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images

arXiv:2208. 00657v2 Announce Type: cross Abstract: Building detection and change detection using remote sensing images can help urban and rescue planning.

By Amir Mohammadian, Foad Ghaderi

llms benchmarks

arXiv Computer Vision

Sep 16

DisasterInsight: A Multimodal Benchmark for Function-Aware and Grounded Disaster Assessment

arXiv:2601.18493v2 Announce Type: replace Abstract: Vision--language models (VLMs) show promise for disaster-response remote sensing, but existing benchmarks mainly emphasize scene-level or damage-ce...

By Sara Tehrani, Yonghao Xu, Leif Haglund, Amanda Berg, Gulnaz Zhambulova, Michael Felsberg

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 21

DMFNet: Dual-Backbone Multiscale Fusion Network for Urban Scene Classification

arXiv:2607. 16338v1 Announce Type: cross Abstract: This article presents DMFNet, a dual-backbone multiscale feature fusion framework with residual feature propagation and spatial attention for remote sensing scene classification.

By Anamitra Ghosh, Abhiroop Chatterjee, Susmita Ghosh

fine-tuning benchmarks

arXiv Computer Vision

Sep 21

DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response

DisasterInsight is a building‑centric benchmark designed to evaluate vision‑language models (VLMs) for disaster response. Built on the xBD satellite dataset, it adds OpenStreetMap‑derived functional labels to 134,108 building instances and offers 15 task types, including instance assessment, scene counting, multi‑instance reasoning, and structured report generation. Experiments show that VLMs excel at visible damage detection but struggle with building function, multi‑instance reasoning, counting, and grounded reporting, and instruction tuning only partially mitigates these gaps.

By Sara Tehrani, Yonghao Xu, Leif Haglund, Amanda Berg, Gulnaz Zhambulova, Michael Felsberg

llms fine-tuning benchmarks

arXiv Computer Vision

Sep 17

A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification

arXiv:2606.10174v2 Announce Type: replace Abstract: Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we i...

By Emadeldeen Hamdan, Yingyi Luo, B. Ugur Toreyin, Erdem Koyuncu, Adam J. Watts, Ugur Gudukbay, Ahmet Enis Cetin

llms benchmarks

arXiv Computer Vision

Aug 21

UAV-Based Infrastructure Inspections: A Literature Review and Proposed Framework for AEC+FM

arXiv:2601. 11665v3 Announce Type: replace Abstract: Unmanned Aerial Vehicles (UAVs) are transforming infrastructure inspections in the Architecture, Engineering, Construction, and Facility Management (AEC+FM) domain.

By Amir Farzin Nikkhah, Dong Chen, Bradford Campbell, Somayeh Asadi, Arsalan Heydarian

llms computer-vision multimodal

arXiv Machine Learning

Aug 24

Keep Your Friends Close, and the Right Neighbours Closer: Disaster-Conditioned Kernel-Regularized Graph Attention for Building Damage Classification

arXiv:2608.20548v1 Announce Type: cross Abstract: Disaster damage is spatial: buildings rarely fail in isolation. Yet using spatial context for damage classification remains surprisingly underexplore...

By Fuad Hasan, Chul Min Yeum

arXiv Computation and Language

Aug 25

DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery

DamageScope is a retrieval‑augmented framework that combines satellite imagery, Vision‑Language Models (VLMs), and Large Language Models (LLMs) to automate property damage assessment after natural disasters. It uses a Retrieval‑Augmented Generation (RAG) architecture to extract structured visual representations from satellite images, enabling interactive natural language queries. The system introduces a multi‑vector embedding‑based clustering algorithm that improves scalability and reduces indexing time by up to 14×, and a dual‑store data architecture that cuts LLM API calls, lowering operational cost and response latency by roughly 3×.

By Ravi K. Rajendran, Biplob Debnath, Murugan Sankaradas, Srimat T. Chakradhar

llms rag multimodal safety

arXiv Machine Learning

Jun 26

Topology-Informed Neural Networks for Flood Detection in Optical and Synthetic Aperture Radar Imagery

arXiv:2606. 26204v1 Announce Type: new Abstract: Floods frequently impact regions around the world.

By Sophia Li, Max Zhao, Raghu G. Raj, Tianyu Chen

llms computer-vision safety

Related stories