# Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery

arXiv: https://arxiv.org/abs/2509.20628 · HTML: https://mlsanity.com/paper/2509.20628/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 18 of 18)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response](https://mlsanity.com/paper/2505.21089/) (2025)
- [Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images](https://mlsanity.com/paper/2208.02205/) (2022)
- [ChangeChat: An Interactive Model for Remote Sensing Change Analysis via Multimodal Instruction Tuning](https://mlsanity.com/paper/2409.08582/) (2024)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives](https://mlsanity.com/paper/2505.14361/) (2025)
- [SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images](https://mlsanity.com/paper/2208.00657/) (2022)
- [ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation](https://mlsanity.com/paper/2404.12606/) (2024)
- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models](https://mlsanity.com/paper/2504.09066/) (2025)
- [DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization](https://mlsanity.com/paper/2504.11637/) (2025)
- [Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment](https://mlsanity.com/paper/2006.05575/) (2020)
- [Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach](https://mlsanity.com/paper/2404.07399/) (2024)
- [Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models](https://mlsanity.com/paper/2509.01895/) (2025)
- [Effective Damage Data Generation by Fusing Imagery with Human Knowledge Using Vision-Language Models](https://mlsanity.com/paper/2508.01380/) (2025)
- [Street View Data Collection Design for Disaster Reconnaissance](https://mlsanity.com/paper/2308.06284/) (2023)
- [Open-source data pipeline for street-view images: a case study on community mobility during COVID-19 pandemic](https://mlsanity.com/paper/2401.13087/) (2024)

## Built on by (most cited first, 2 of 2)

- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface](https://mlsanity.com/paper/2512.20813/) (2025)

## Abstract (arXiv)

Building-level occupancy after disasters is vital for triage, inspections, utility re-energization, and equitable resource allocation. Overhead imagery provides rapid coverage but often misses facade and access cues that determine habitability, while street-view imagery captures those details but is sparse and difficult to align with parcels. We present FacadeTrack, a street-level, language-guided framework that links panoramic video to parcels, rectifies views to facades, and elicits interpretable attributes (for example, entry blockage, temporary coverings, localized debris) that drive two decision strategies: a transparent one-stage rule and a two-stage design that separates perception from conservative reasoning. Evaluated across two post-Hurricane Helene surveys, the two-stage approach achieves a precision of 0.927, a recall of 0.781, and an F-1 score of 0.848, compared with the one-stage baseline at a precision of 0.943, a recall of 0.728, and an F-1 score of 0.822. Beyond accuracy, intermediate attributes and spatial diagnostics reveal where and why residual errors occur, enabling targeted quality control. The pipeline provides auditable, scalable occupancy assessments suitable for integration into geospatial and emergency-management workflows.