arXiv Computation and Language

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×.

Hugging Face Trending Papers

Aug 9

Integrated Multimodal AI System for Retrieval-Augmented Reasoning, Object Sensing, and Damage Analysis

This work presents a unified multimodal AI system for damage assessment that integrates retrieval-augmented generation (RAG) models, thermal spectrum perception, vision foundation model pipelines, and exploratory wireless signal sensing. A RAG component is developed to ground a locally hosted language model in project-specific documentation, including specialized damage level classification criteria to mitigate hallucinations during inference.

llms rag computer-vision multimodal safety

arXiv AI

Aug 11

Integrated Multimodal AI System for Retrieval-Augmented Reasoning, Object Sensing, and Damage Analysis

arXiv:2608. 08935v1 Announce Type: new Abstract: This work presents a unified multimodal AI system for damage assessment that integrates retrieval-augmented generation (RAG) models, thermal spectrum perception, vision foundation model pipelines, and exploratory wireless signal sensing.

By Kalelo Dukuray, Israel Pina, Evan Perez, Erika Ardiles-Cruz, Jie Wei

llms rag computer-vision multimodal safety

arXiv Computer Vision

2d ago

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

Jun 16

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.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

multimodal

arXiv AI

Jun 30

Multimodal and Multiscale Spatial-Temporal Semantic Search and Recommendation with AI Foundation Models

arXiv:2606. 28369v1 Announce Type: cross Abstract: Semantic search and recommendation of similar documents, such as news and reports about unusual environmental events (e.

By Yuanyuan Tian, Wenwen Li, Xiao Chen, Michael Brook, Michael Brubaker, Anna Liljedahl, Chitta Baral

llms rag multimodal

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 26

On-board Remote-Sensing Foundation Models for Unsupervised Change Detection of Disaster Events

arXiv:2606. 27018v1 Announce Type: cross Abstract: Remote Sensing Foundation Models (RSFMs) have emerged as a powerful alternative to supervised models for Earth Observation, allowing satellites to autonomously trigger high-resolution captures or adjust tasking parameters upon detecting an anomaly, thereby maximizing the utility of the mission's limited power and computational resources.

By S. Ram\'irez-Gallego

diffusion

arXiv Computer Vision

Aug 31

GeBDA: Building Damage Assessment as Text-Based Sequence Prediction

The paper proposes GeBDA, a method that treats Building Damage Assessment as a text-based sequence prediction task using a Vision‑Language Model. Instead of specialized architectures, it uses an autoregressive VLM to output a variable‑length list of bounding boxes with coordinates and damage labels. The authors demonstrate promising results on bi‑temporal satellite images using the open Gemma model and a tailored text prompt.

By Olivier Dietrich, Krishna Sapkota, Konrad Schindler, Genady Beryozkin

llms fine-tuning multimodal

arXiv Machine Learning

Jul 20

More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe

arXiv:2607. 15942v1 Announce Type: cross Abstract: Remote sensing vision-language models are increasingly expected to support open-ended reasoning over Earth Observation data and a variety of tasks.

By Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")

llms computer-vision reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Aug 5

Geo-Embed: Towards Unified Multimodal Embeddings for Urban Understanding

arXiv:2608. 03826v1 Announce Type: cross Abstract: Geospatial and urban applications increasingly require models to compare heterogeneous evidence across street-view imagery, remote-sensing observations, text descriptions, region proposals, and temporal change cues.

By Jiapeng Li, Yong Li, Junjie Zhou, Fan Zhang, Yu Liu

rag nlp multimodal benchmarks

arXiv AI

Jul 29

DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization

arXiv:2607. 24856v1 Announce Type: cross Abstract: Social media imagery (SMI) provides timely and fine-grained ground perspectives that are valuable for situational awareness and emergency response.

By Wenping Yin, Ziqi Liu, Naixia Mou, Weijia Li, Danfeng Hong, Hao Li

llms multimodal benchmarks

arXiv AI

Jul 16

Post-Disaster Affected Area Segmentation with a Vision Transformer (ViT)-based EVAP Model using Sentinel-2 and Formosat-5 Imagery

arXiv:2507. 16849v3 Announce Type: replace-cross Abstract: We propose a vision transformer (ViT)-based deep learning framework to refine disaster-affected area segmentation from remote sensing imagery, aiming to support and enhance the Emergent Value Added Product (EVAP) developed by the Taiwan Space Agency (TASA).

By Yi-Shan Chu, Hsuan-Cheng Wei

llms computer-vision

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