VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis
This work addresses real-time damage assessment for disaster-affected areas, but it is incremental as it focuses on dataset creation and baseline comparisons.
The paper tackles post-disaster damage assessment by developing a VQA dataset called HurMic-VQA from hurricane Michael and evaluating baseline VQA models to speed up recovery processes.
Visual Question Answering system integrated with Unmanned Aerial Vehicle (UAV) has a lot of potentials to advance the post-disaster damage assessment purpose. Providing assistance to affected areas is highly dependent on real-time data assessment and analysis. Scope of the Visual Question Answering is to understand the scene and provide query related answer which certainly faster the recovery process after any disaster. In this work, we address the importance of \textit{visual question answering (VQA)} task for post-disaster damage assessment by presenting our recently developed VQA dataset called \textit{HurMic-VQA} collected during hurricane Michael, and comparing the performances of baseline VQA models.