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🛰️ BiTemporal-StreetView-Damage

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

Study Area Map


📌 Introduction

This repository presents a novel framework for bi-temporal street-view image analysis, aimed at advancing hyperlocal disaster damage assessment. We integrate pre- and post-disaster imagery using pre-trained vision and vision-language models to classify and localize disaster impact more accurately.

🔍 Key Contributions

  • Dual-channel model for fusing pre- and post-disaster street-view images.
  • 📸 2,249 labeled street-view image pairs, annotated with fine-grained disaster impact.
  • 📈 Performance: Accuracy improved from 66.14% (post-only) to 77.11% (bi-temporal).
  • 🔥 Grad-CAM visualization confirms the added value of pre-disaster imagery for model focus.
  • 🏙️ Enables rapid and fine-grained damage mapping, supporting climate-resilient urban planning.

Dual-Channel Architecture

Figure: Dual-channel architecture for bi-temporal disaster damage assessment.

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📂 Dataset

You can access the bi-temporal street-view disaster dataset from the following DOI:

📁 Yang, Yifan (2025).
Perceiving Multidimensional Disaster Damages from Street–View Images Using Visual–Language Models.
figshare. Dataset. https://doi.org/10.6084/m9.figshare.28801208.v2

The dataset includes:

  • Pre- and post-disaster images
  • Location and damage type annotations
  • Severity scores (minor, moderate, severe)
  • Sample image regions from Horseshoe Beach, Florida after Hurricane Idalia and Milton

🧠 Paper Reference

Yang, Y., Zou, L., Zhou, B., Li, D., Lin, B., Abedin, J., & Yang, M. (2025).
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models.
arXiv preprint arXiv:2504.09066


🗂 Repository Structure

BiTemporal-StreetView-Damage/
│
├── codes/                          # Model training and evaluation scripts
├── images/                         # Project figures
│   ├── study_area_disaster_damage_made.png
│   ├── architect1.drawio (1).png
│   ├── design experiment.drawio (1).png
│   ├── dual_channel.drawio (2).png
│   ├── 0204-06.png
│   ├── readme.txt
├── LICENSE
├── README.md
└── .gitignore

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

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