Conference Paper2021Cited by 18

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

  • Alumni, CCDS

  • PhD Student

    University of California, Riverside, USA

  • BSc

    Bangladesh University of Engineering and Technology

Published at

WACV, ICPR, ICIP, Pattern Recognition

Abu Bakar Siddik Nayem
Abu Bakar Siddik Nayem

Abu Bakar Siddik Nayem

  • Alumni, CCDS

  • Master's Student

    BTU Cottbus-Senftenberg, Germany

  • Lead ML Engineer

    Acme AI, Bangladesh

  • BSc

    Independent University, Bangladesh (IUB)

Published at

ICPR, ICIP, Scientific Reports

Md Anis Sarker
Anis Sarker

Md Anis Sarker

  • Alumni, CCDS

  • ML Engineer

    Acme AI, Bangladesh

  • AI Engineer

    AinoviQ

  • BSc

    Independent University, Bangladesh (IUB)

Published at

ICPR, ICIP, Scientific Reports

Ovi Paul
Ovi Paul

Ovi Paul

  • Alumni, CCDS

  • PhD Student

    University of Houston, USA

  • BSc

    Independent University, Bangladesh (IUB)

Published at

ICPR, ICIP, Scientific Reports

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

Prof. M Ashraful Amin, PhD

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

Published at

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

Prof. Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

  • Director, AI/ML Wing
  • Supervisor, ComBio Wing

  • Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

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

Moinul I Zaber, PhD
Moinul I Zaber, PhD

Moinul I Zaber, PhD

  • Director, HCI Wing
  • Supervisor, DS Wing

  • Professor

    Department of Computer Science & Engineering

    University of Dhaka, Bangladesh

  • Visiting Academic

    Department of Computer Science and Technology

    University of Cambridge, United Kingdom

  • Research Fellow

    Institute of Development Studies (Sussex), United Kingdom

Published at

ICPR, HCII, JMIR

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 (IUB)

Published at

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

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Cited By

  1. 1.Sreeparna Guha, Rabin K. Jana, Manas K. Sanyal. (2022). Artificial neural network approaches for disaster management: A literature review. International Journal of Disaster Risk Reduction, 81, 103276[10.1016/j.ijdrr.2022.103276]
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  4. 4.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]
  5. 5.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]
  6. 6.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]
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  13. 13.Jieli Chen, Hanyue Xu, Li Minn Ang, Kah Phooi Seng. (2025). Distributed Situation Awareness System Using Vision Transformer with Attention Maps for Natural Disasters. International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), 1–4[10.1109/icest66328.2025.11098198]
  14. 14.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]
  15. 15.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]
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