Luo, K.; Lian, I.-B. Building a Vision Transformer-Based Damage Severity Classifier with Ground-Level Imagery of Homes Affected by California Wildfires. Fire2024, 7, 133.
Luo, K.; Lian, I.-B. Building a Vision Transformer-Based Damage Severity Classifier with Ground-Level Imagery of Homes Affected by California Wildfires. Fire 2024, 7, 133.
Luo, K.; Lian, I.-B. Building a Vision Transformer-Based Damage Severity Classifier with Ground-Level Imagery of Homes Affected by California Wildfires. Fire2024, 7, 133.
Luo, K.; Lian, I.-B. Building a Vision Transformer-Based Damage Severity Classifier with Ground-Level Imagery of Homes Affected by California Wildfires. Fire 2024, 7, 133.
Abstract
The rise in both the frequency of natural disasters and the ubiquity of artificial intelligence has led to novel applications of new technologies in improving disaster response processes, such as the labor-intensive assessment of disaster damages. Assessment of residential and commercial structure damages, a precursory step to government agencies being able to provide most of their financial assistance, has benefited from aerial and satellite imagery-based computer vision models; however, limitations of using such imagery include the ground structures being obscured by clouds or smoke, as well as the lack of resolution to distinguish individual structures from others. Using a different data source, we propose a damage severity classification model using ground-level imagery, focusing on residential structures damaged by wildfires. This classifier, a Vision Transformer (ViT) model trained on over 18,000 professionally labeled images of damaged homes from the 2020-2022 California wildfires, has achieved an accuracy score of over 95%. Further, we have open sourced the training dataset–the first of its kind and scale–as well as built a publicly available web application prototype, which we demoed to and received feedback from disaster response officials, both of which further contribute to the broader literature beyond the proposed model.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
