Conference Paper2021Cited by 15

A Novel Disaster Image Data-set and Characteristics Analysis using Attention Model

Fahim Faisal Niloy, Arif, Abu Bakar Siddik Nayem, Anis Sarker, Ovi Paul, M Ashraful Amin, Amin Ahsan Ali, Moinul Islam Zaber, AKM Mahbubur Rahman

2020 25th International Conference on Pattern Recognition (ICPR)

IEEE, pp. 6116-6122

DOI: 10.1109/icpr48806.2021.9412504Open AccessURL

Abstract

The advancement of deep learning technology has enabled us to develop systems that outperform any other classification technique. However, success of any empirical system depends on the quality and diversity of the data available to train the proposed system. In this research, we have carefully accumulated a relatively challenging dataset that contains images collected from various sources for three different disasters: fire, water and land. Besides this, we have also collected images for various damaged infrastructure due to natural or man made calamities and damaged human due to war or accidents. We have also accumulated image data for a class named non-damage that contains images with no such disaster or sign of damage in them. There are 13,720 manually annotated images in this dataset, each image is annotated by three individuals. We are also providing discriminating image class information annotated manually with bounding box for a set of 200 test images. Images are collected from different news portals, social media, and standard datasets made available by other researchers. A three layer attention model (TLAM) is trained and average five fold validation accuracy of 95.88% is achieved. Moreover, on the 200 unseen test images this accuracy is 96.48%. We also generate and compare attention maps for these test images to determine the characteristics of the trained attention model.

Keywords

  • infrastructure damage
  • water-related disasters
  • attention mechanism
  • attention maps
  • human damage

CCDS Authors

Fahim Faisal Niloy
Fahim Faisal Niloy

Fahim Faisal Niloy

  • Research Assistant, CCDS

PhD Student

University of California Riverside

Published at

WACV, ICPR, ICIP, Pattern Recognition

Research Interests

Machine Learning

Abu Bakar Siddik Nayem
Abu Bakar Siddik Nayem

Abu Bakar Siddik Nayem

  • Research Assistant, CCDS

Lead ML Engineer

Acme AI

Published at

ICPR, ICIP, Scientific Reports

Research Interests

Machine Learning

Anis Sarker
Anis Sarker

Anis Sarker

  • Research Assistant, CCDS

AI Engineer

AinoviQ

Published at

ICPR, ICIP, Scientific Reports

Research Interests

Machine Learning

Ovi Paul
Ovi Paul

Ovi Paul

  • Research Assistant, CCDS

PhD Student

University of Houston

Published at

ICPR, ICIP, Scientific Reports

Research Interests

Machine Learning, Geosensing System Engineering

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

Moinul I Zaber, PhD
Moinul I Zaber, PhD

Moinul I Zaber, PhD

  • Director, HCI Wing
  • Supervisor, DS Wing

Professor

+2 more affiliations

Department of Computer Science & Engineering

Dhaka University

Published at

ICPR, HCII, JMIR

Research Interests

Computational Social Science, Human-Computer Interaction, Telecommunication Policy

AKM Mahbubur Rahman, PhD
AKM Mahbubur Rhman, PhD

AKM Mahbubur Rahman, PhD

  • Director, DS & IPT Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, ECCV, WACV, PAKDD, LREC, IJCNN, ICPR, ICIP, HCII, BEA, Scientific Reports, Information Sciences

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Cited By

  1. 1.Kibitok Abraham, Moataz Abdelwahab, Mohammed Abo-Zahhad. (2023). Classification and detection of natural disasters using machine learning and deep learning techniques: A review. Earth Science Informatics, 17(2), 869–891[10.1007/s12145-023-01205-2]
  2. 2.Zhihao Ma, Mengke Yuan, Jiaming Gu, Weiliang Meng, Shibiao Xu, Xiaopeng Zhang. (2022). Triple-strip attention mechanism-based natural disaster images classification and segmentation. The Visual Computer, 38(9-10), 3163–3173[10.1007/s00371-022-02535-w]
  3. 3.Payal Bhadra, Avijit Balabantaray, Ajit Kumar Pasayat. (2023). MFEMANet: an effective disaster image classification approach for practical risk assessment. Machine Vision and Applications, 34(5)[10.1007/s00138-023-01430-1]
  4. 4.Yikun Zhao, Lei Feng, Fanqin Zhou, Wenjing Li, Zehui Xiong, Hongyang Du, Celimuge Wu, Song Guo, Tony Q. S. Quek, Zhu Han. (2025). Energy-Efficient Over-the-Air Computation for Federated Generative Model Fine-Tuning in Unmanned Vehicle-Assisted Disaster Relief. IEEE Transactions on Cognitive Communications and Networking, 12, 56–73[10.1109/tccn.2025.3553315]
  5. 5.Kibitok Abraham, Mohammed Abo-Zahhad, Moataz Abdelwahab. (2024). Natural disaster damage analysis using lightweight spatial feature aggregated deep learning model. Earth Science Informatics, 17(4), 3149–3161[10.1007/s12145-024-01325-3]
  6. 6.Tanu Gupta, Sudip Roy. (2024). DisasterRes-Net: A framework for analyzing social media images in disaster response. International Journal of Disaster Risk Reduction, 116, 105119[10.1016/j.ijdrr.2024.105119]
  7. 7.Hao Liu, Jiancheng An, Qian Ma, Lu Gan, Mehdi Bennis, Mérouane Debbah, Tie Jun Cui. (2025). A Novel Hybrid Optical-Electronic Neural Network Approach to Task-Oriented Semantic Communications. , 2418–2423[10.1109/icc52391.2025.11160907]
  8. 8.Sohail Abbas, Manar Abu Talib, Qasim Nasir, Omar Belal, Mohammed Emad Al-Haidary, Ibtihal Ahmed. (2025). A survey of disaster management datasets. Journal of Information and Telecommunication, 9(4), 574–596[10.1080/24751839.2025.2509452]
  9. 9.Kunshan Yang, Wenwei Luo, Yuguo Hu, Jiafu Yan, Mengmeng Jing, Lin Zuo. (2025). A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition. , 1–6[10.1109/icme59968.2025.11210079]
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  11. 11.Gao Yu Lee, Tanmoy Dam, Md. Meftahul Ferdaus, Daniel Puiu Poenar, Vu Duong. (2026). Enhancing Few-Shot Classification of Disaster Imagery with ABHFA-Net. SN Computer Science, 7(6)[10.1007/s42979-026-05262-4]
  12. 12.Yachun He, Jidong Wu, Lili Wang, Yingjun Xu, Youtian Yang, Wei Wu, Wei Jiang. (2026). ViGeo-Fuse: A multimodal framework integrating computer vision and geospatial ML for post-earthquake building damage classification. Developments in the Built Environment, 27, 101001[10.1016/j.dibe.2026.101001]
  13. 13.Samiksha Bharti, Bappaditya Das. (2026). Explainable Quantum-Optimized Efficient Net Framework For Multi-Class Disaster Image Classification. International Journal of Innovations in Science Engineering and Management., 198–208[10.69968/ijisem.2026v5i1198-208]
  14. 14.Masao KUBO, Quyet Thanh NGUYEN, Hiroshi SATO, Akihiro YAMAGUCHI. (2026). Disaster Related Image Detection on an IoT Device Using Vision-language Technology. Transactions of the Society of Instrument and Control Engineers, 62(2), 59–68[10.9746/sicetr.62.59]
  15. 15.Alain Patrick Ndigande, Josiah Wiggins, Sedat Ozer. (2026). Survey on Disaster Management Datasets for Remote Sensing-Based Emergency Applications. IEEE Transactions on Geoscience and Remote Sensing, 64, 4705121[10.1109/tgrs.2026.3692760]
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