mlsanityData to 7 Oct 2026

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

arXiv 2106.10548 ↗First posted 19 Jun 2021cs.CVcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 19 Jun 2021.

Builds on

11 of 19 references.

References · 11

  1. 2016Hierarchical Question-Image Co-Attention for Visual Question Answeringcited by 278
  2. 2015Stacked Attention Networks for Image Question Answeringcited by 273
  3. 2016Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Groundingcited by 243
  4. 2020FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understandingcited by 181
  5. 2019xBD: A Dataset for Assessing Building Damage from Satellite Imagerycited by 138
  6. 2017Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answeringcited by 117
  7. 2015Where To Look: Focus Regions for Visual Question Answeringcited by 64
  8. 2015Simple Baseline for Visual Question Answeringcited by 24
  9. 2015ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answeringcited by 23
  10. 2018Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagerycited by 22
  11. 2020Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessmentcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 1; 2025: 6; 2026: 3.

15 papers.

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  1. 2023From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunitiescited by 24
  2. 2023Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMscited by 14
  3. 2023Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenariocited by 10
  4. 2024TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devicescited by 5
  5. 2025ZeShot-VQA: Zero-Shot Visual Question Answering Framework with Answer Mapping for Natural Disaster Damage Assessmentcited by 4
  6. 20253DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessmentcited by 3
  7. 2022Continual VQA for Disaster Response Systemscited by 2
  8. 2023Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Regioncited by 1
  9. 2025Think First, Assign Next (ThiFAN-VQA): A Two-stage Chain-of-Thought Framework for Post-Disaster Damage Assessmentcited by 1
  10. 2025Towards Accurate UAV Image Perception: Guiding Vision-Language Models with Stronger Task Promptscited by 1
  11. 2026From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagerycited by 1
  12. 2025VLCE: A Knowledge-Enhanced Framework for Image Description in Disaster Assessmentcited by 0
3 more
  1. 2025An Efficient and Effective Encoder Model for Vision and Language Tasks in the Remote Sensing Domaincited by 0
  2. 2026Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applicationscited by 0
  3. 2026GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligencecited by 0