# Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models

arXiv: https://arxiv.org/abs/2504.09066 · HTML: https://mlsanity.com/paper/2504.09066/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models](https://mlsanity.com/paper/2402.02207/) (2024)
- [GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model](https://mlsanity.com/paper/2406.18572/) (2024)
- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [GeoLocator: a location-integrated large multimodal model for inferring geo-privacy](https://mlsanity.com/paper/2311.13018/) (2023)

## Built on by (most cited first, 8 of 8)

- [Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach](https://mlsanity.com/paper/2512.20056/) (2025)
- [Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models](https://mlsanity.com/paper/2509.01895/) (2025)
- [Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery](https://mlsanity.com/paper/2509.20628/) (2025)
- [Satellite-to-Street: Synthesizing Post-Disaster Views from Satellite Imagery via Generative Vision Models](https://mlsanity.com/paper/2603.20697/) (2026)
- [RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery](https://mlsanity.com/paper/2606.21819/) (2026)
- [DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery](https://mlsanity.com/paper/2603.14837/) (2026)
- [RAPIDMap: Rapid Multi-Agent Pipeline for Interpretable Disaster Mapping from Satellite and Street-view Imagery](https://mlsanity.com/paper/2609.00046/) (2026)
- [When Satellite and Street Views Disagree: Cross-View Complementarity in Disaster Damage Assessment](https://mlsanity.com/paper/2610.04327/) (2026)

## Abstract (arXiv)

Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on-the-ground insights. Despite several investigations attempting to analyze street-view images for damage estimation, they mainly focus on post-disaster images. The potential of time-series street-view images remains underexplored. Pre-disaster images provide valuable benchmarks for accurate damage estimations at building and street levels. These images could aid annotators in objectively labeling post-disaster impacts, improving the reliability of labeled data sets for model training, and potentially enhancing the model performance in damage evaluation. The goal of this study is to estimate hyperlocal, on-the-ground disaster damages using bi-temporal street-view images and advanced pre-trained vision models. Street-view images before and after 2024 Hurricane Milton in Horseshoe Beach, Florida, were collected for experiments. The objectives are: (1) to assess the performance gains of incorporating pre-disaster street-view images as a no-damage category in fine-tuning pre-trained models, including Swin Transformer and ConvNeXt, for damage level classification; (2) to design and evaluate a dual-channel algorithm that reads pair-wise pre- and post-disaster street-view images for hyperlocal damage assessment. The results indicate that incorporating pre-disaster street-view images and employing a dual-channel processing framework can significantly enhance damage assessment accuracy. The accuracy improves from 66.14% with the Swin Transformer baseline to 77.11% with the dual-channel Feature-Fusion ConvNeXt model. This research enables rapid, operational damage assessments at hyperlocal spatial resolutions, providing valuable insights to support effective decision-making in disaster management and resilience planning.