DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery
- URL: http://arxiv.org/abs/2608.21529v1
- Date: Fri, 21 Aug 2026 18:09:21 GMT
- Title: DamageScope: Vision-Language Retrieval at Scale for Disaster Damage Assessment from Satellite Imagery
- Abstract summary: DamageScope is a retrieval-augmented framework that combines satellite imagery with Vision-Language Models (VLMs) and Large Language Models (LLMs)<n>Built on a Retrieval-Augmented Generation (RAG) framework, DamageScope extracts structured visual representations from satellite imagery to support interactive natural language queries for damage assessment.
- Score: 6.514592086157328
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Timely and accurate assessment of property damage is critical following natural disasters. Traditional on-site inspections are labor-intensive, costly, and often pose safety risks. Advances in satellite imagery and vision-language models (VLMs) enable scalable remote damage assessment; however, integrating VLMs into large-scale Earth observation pipelines presents challenges in computational efficiency, data organization, and information retrieval. To address these challenges, we present DamageScope, a retrieval-augmented framework that combines satellite imagery with Vision-Language Models (VLMs) and Large Language Models (LLMs) to automate property damage analysis. Built on a Retrieval-Augmented Generation (RAG) framework, DamageScope extracts structured visual representations from satellite imagery to support interactive natural language queries for damage assessment. To address scalability, we introduce a novel multi-vector embedding-based clustering algorithm that outperforms traditional single-vector embedding approaches while reducing indexing time by up to 14x. Furthermore, a dual-store data architecture minimizes LLM API calls, reducing both operational cost and response latency by up to approximately 3x. By effectively balancing scalability and operational efficiency, DamageScope provides a robust and practical solution for real-world damage assessment tasks.
Related papers
- Optimizing Latent Representations for Robust Building Damage Assessment Onboard Earth Observation Satellites [0.0]
We propose an original AI-based system that enables object-level building damage assessment (localization and damage classification) directly onboard satellites.<n>Available pre-disaster images are encoded on ground into compact latent representations, transmitted to the satellite, and compared on-board with newly acquired post-disaster observations.<n>We explore the design space through a systematic benchmark of onboard-compatible variants, analyzing the impact of siamese processing, cross-attention, latent-space compression, and robustness-oriented data augmentation.
arXiv Detail & Related papers (2026-05-28T08:21:14Z) - Event-based SLAM Benchmark for High-Speed Maneuvers [68.148886127117]
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution.<n>Existing event-based approaches, although successful in mitigating motion blur caused by high-speed maneuvers, suffer from many limitations.<n>We introduce a benchmarking framework for event-based state estimation, called EvSLAM, characterized by sufficient variation in data collection platforms.
arXiv Detail & Related papers (2026-04-27T04:36:41Z) - JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees [64.57426735981902]
In maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions.<n>To enable fault trees stored as images to be directly processed by large language models, we propose a novel textual representation of fault trees.<n>We construct a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments.
arXiv Detail & Related papers (2026-03-24T09:13:43Z) - From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery [0.0]
We propose a novel framework that integrates AI-based super-resolution, deep learning object detection, and Vision-Language Models (VLMs) for comprehensive post-disaster building damage assessment.<n>First, we enhance pre- and post-disaster satellite imagery using a Video Restoration Transformer (VRT) to upscale images from 1024x1024 to 4096x4096 resolution.<n>We employ CLIPScore for reference-free semantic alignment and introduce a multi-model VLM-as-a-Jury strategy to reduce individual model bias in safety-critical decision making.
arXiv Detail & Related papers (2026-03-24T03:50:41Z) - DisasterInsight: A Multimodal Benchmark for Function-Aware and Grounded Disaster Assessment [19.434058305975167]
DisasterInsight is a benchmark designed to evaluate vision-language models (VLMs) on realistic disaster analysis tasks.<n>It restructures the xBD dataset into approximately 112K building-centered instances.<n>It supports instruction-diverse evaluation across multiple tasks, including building-function classification, damage-level and disaster-type classification, counting, and structured report generation aligned with humanitarian assessment guidelines.
arXiv Detail & Related papers (2026-01-26T13:48:11Z) - Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers [0.0]
We introduce a framework that combines high-resolution post-earthquake satellite imagery with building-specific metadata relevant to the seismic performance of the structure.<n>Our model achieves state-of-the-art performance in multiclass post-earthquake damage identification for buildings from the Turkey-Syria earthquake on February 6, 2023.
arXiv Detail & Related papers (2024-12-05T23:19:51Z) - Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation [60.80423207808076]
Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation.<n>We propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations.<n>We build HRVMamba, a novel model for efficient high-resolution representation learning.
arXiv Detail & Related papers (2024-10-04T06:19:29Z) - Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks.
However, their robustness under natural and adversarial perturbations remains a critical concern.
We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
arXiv Detail & Related papers (2024-06-13T17:59:44Z) - Assessing out-of-domain generalization for robust building damage
detection [78.6363825307044]
Building damage detection can be automated by applying computer vision techniques to satellite imagery.
Models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event.
We argue that future work should focus on the OOD regime instead.
arXiv Detail & Related papers (2020-11-20T10:30:43Z) - RescueNet: Joint Building Segmentation and Damage Assessment from
Satellite Imagery [83.49145695899388]
RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end.
RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
arXiv Detail & Related papers (2020-04-15T19:52:09Z) - An Attention-Based System for Damage Assessment Using Satellite Imagery [18.43310705820528]
We present Siam-U-Net-Attn model - a multi-class deep learning model with an attention mechanism - to assess damage levels of buildings.
We evaluate the proposed method on xView2, a large-scale building damage assessment dataset, and demonstrate that the proposed approach achieves accurate damage scale classification and building segmentation results simultaneously.
arXiv Detail & Related papers (2020-04-14T16:37:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.