Accelerating Post-Tornado Disaster Assessment Using Advanced Deep Learning Models

TL;DR

This paper presents a deep learning-based system using YOLOv11 and ResNet50 to automate and speed up post-tornado disaster assessments, improving accuracy and efficiency in damage analysis.

Contribution

It introduces a novel automated assessment system employing advanced computer vision models for rapid post-disaster damage evaluation.

Findings

ResNet50 achieved 90.28% accuracy in damage classification

Inference time per image was 1529ms

System demonstrates scalable and efficient damage assessment capabilities

Abstract

Post-disaster assessments of buildings and infrastructure are crucial for both immediate recovery efforts and long-term resilience planning. This research introduces an innovative approach to automating post-disaster assessments through advanced deep learning models. Our proposed system employs state-of-the-art computer vision techniques (YOLOv11 and ResNet50) to rapidly analyze images and videos from disaster sites, extracting critical information about building characteristics, including damage level of structural components and the extent of damage. Our experimental results show promising performance, with ResNet50 achieving 90.28% accuracy and an inference time of 1529ms per image on multiclass damage classification. This study contributes to the field of disaster management by offering a scalable, efficient, and objective tool for post-disaster analysis, potentially capable of transforming how communities and authorities respond to and learn from catastrophic events.