# VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis

arXiv: https://arxiv.org/abs/2106.10548 · HTML: https://mlsanity.com/paper/2106.10548/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 19 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Stacked Attention Networks for Image Question Answering](https://mlsanity.com/paper/1511.02274/) (2015)
- [Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding](https://mlsanity.com/paper/1606.01847/) (2016)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering](https://mlsanity.com/paper/1708.01471/) (2017)
- [Where To Look: Focus Regions for Visual Question Answering](https://mlsanity.com/paper/1511.07394/) (2015)
- [ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering](https://mlsanity.com/paper/1511.05960/) (2015)
- [Simple Baseline for Visual Question Answering](https://mlsanity.com/paper/1512.02167/) (2015)
- [Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery](https://mlsanity.com/paper/1812.01756/) (2018)
- [Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2009.01193/) (2020)

## Built on by (most cited first, 15 of 15)

- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
- [Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMs](https://mlsanity.com/paper/2310.16033/) (2023)
- [Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario](https://mlsanity.com/paper/2312.01882/) (2023)
- [TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices](https://mlsanity.com/paper/2404.03574/) (2024)
- [ZeShot-VQA: Zero-Shot Visual Question Answering Framework with Answer Mapping for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2506.00238/) (2025)
- [3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment](https://mlsanity.com/paper/2509.11097/) (2025)
- [Continual VQA for Disaster Response Systems](https://mlsanity.com/paper/2209.10320/) (2022)
- [Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region](https://mlsanity.com/paper/2303.07403/) (2023)
- [Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessment](https://mlsanity.com/paper/2511.19557/) (2025)
- [Towards Accurate UAV Image Perception: Guiding Vision-Language Models with Stronger Task Prompts](https://mlsanity.com/paper/2512.07302/) (2025)
- [From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery](https://mlsanity.com/paper/2603.22768/) (2026)
- [VLCE: A Knowledge-Enhanced Framework for Image Description in Disaster Assessment](https://mlsanity.com/paper/2509.21609/) (2025)
- [An Efficient and Effective Encoder Model for Vision and Language Tasks in the Remote Sensing Domain](https://mlsanity.com/paper/2512.15531/) (2025)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)
- [GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence](https://mlsanity.com/paper/2606.17246/) (2026)

## Abstract (arXiv)

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 textitvisual question answering (VQA) task for post-disaster damage assessment by presenting our recently developed VQA dataset called textitHurMic-VQA collected during hurricane Michael, and comparing the performances of baseline VQA models.