Journal article
Feasibility of Visual Question Answering (VQA) for Post-Disaster Damage Detection Using Aerial Footage
Applied Sciences, Vol.13(8), 5079
04/19/2023
Web of Science ID: WOS:000977636000001
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Abstract
Natural disasters are a major source of significant damage and costly repairs around the world. After a natural disaster occurs, there is usually a significant amount of damage, and with that, there are also a lot of costs involved with repairing and aiding all the people involved. In addition, the occurrence of natural phenomena has increased significantly in the past decade. With that in mind, post-disaster damage detection is usually performed manually by human operators. Taking into consideration all the areas one has to closely look into, as well as the difficult terrain and places with hard access, it becomes easy to understand how incredibly difficult it is for a surveyor to identify and annotate every single possible damage out there. Because of that, it has become essential to find new creative solutions for damage detection and classification in the case of natural disasters, especially hurricanes. This study focuses on the feasibility of using a Visual Question Answering (VQA) method for post-disaster damage detection, using aerial footage taken from an Unmanned Aerial Vehicle (UAV). Two other approaches are also utilized to provide comparison and to evaluate the performance of VQA. Our case study on our custom dataset collected after Hurricane Sally shows successful results using VQA for post-disaster damage detection applications.
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Details
- Title
- Feasibility of Visual Question Answering (VQA) for Post-Disaster Damage Detection Using Aerial Footage
- Publication Details
- Applied Sciences, Vol.13(8), 5079
- Resource Type
- Journal article
- Publisher
- MDPI AG; Switzerland
- Number of pages
- 16
- Copyright
- © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Identifiers
- WOS:000977636000001; 99380176594506600
- Academic Unit
- Intelligent Systems and Robotics