research-article

Disaster Assessment from Satellite Imagery by Analysing Topographical Features Using Deep Learning

Authors:

Saramsha Dotel,

Avishekh Shrestha,

Sanjeeb Prasad PandayAuthors Info & Claims

IVSP '20: Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing

Pages 86 - 92

https://doi.org/10.1145/3388818.3389160

Published: 18 May 2020 Publication History

Abstract

This paper explores the application of deep learning techniques in the task of assessing disaster impact from satellite imagery. Identifying the regions impacted by a disaster is critical for effective mobilization of relief efforts. Satellite images, with their vast coverage of ground surface, are a valuable resource that can be leveraged for this purpose. However, the task of analysing a satellite image to detect regions impacted by disaster is challenging. In recent years, the increasing availability of satellite images of a place presents an opportunity to utilize deep learning on these images to provide a preliminary insight on the impact of a disaster after its occurrence. We particularly focus on water related disasters like floods and hurricanes. To identify the impacted regions, we employ Convolutional Neural Networks to semantically segment topographical features like roads on pre and post-disaster satellite images and find the regions with maximal change. However, this approach is less applicable on rural landscapes due to the sparse distribution of topographical features like roads. To address such cases of imagery from rural areas, we propose a bitemporal image classification approach to compare pre and post disaster scenes and directly identify if the regions are impacted or not. On testing against a ground truth satellite image from DigitalGlobe with labeled regions depicting the impacts of Hurricane Harvey, the flooded road extraction approach achieved an accuracy of 0.845 with a F1-Score of 0.675. Similarly, the bitemporal image classification approach registered an accuracy of 0.94 when tested against a rural landscape impacted by South Asian Monsoon Flooding of 2017.

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Cited By

Braik AKoliou M(2024)Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learningComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1319739:15(2389-2404)Online publication date: 29-Mar-2024
https://dl.acm.org/doi/10.1111/mice.13197
Arora GR M B(2024)Disaster Scene Classification with Deep Learning: A Keras-Based Approach Utilizing Robotic Systems2024 IEEE Students Conference on Engineering and Systems (SCES)10.1109/SCES61914.2024.10652476(1-6)Online publication date: 21-Jun-2024
https://doi.org/10.1109/SCES61914.2024.10652476
Bangar AAnsari SAli SBaderao VAlhat B(2024)Disaster Prediction Using Appropriate Machine Learning Techniques2024 IEEE Pune Section International Conference (PuneCon)10.1109/PuneCon63413.2024.10895154(1-7)Online publication date: 13-Dec-2024
https://doi.org/10.1109/PuneCon63413.2024.10895154

Index Terms

Disaster Assessment from Satellite Imagery by Analysing Topographical Features Using Deep Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

IVSP '20: Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing

March 2020

168 pages

ISBN:9781450376952

DOI:10.1145/3388818

Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Nanyang Technological University
The Hong Kong Polytechnic: The Hong Kong Polytechnic University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2020

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University Grants Commission- Nepal

Conference

IVSP '20

IVSP '20: 2020 2nd International Conference on Image, Video and Signal Processing

March 20 - 22, 2020

Singapore, Singapore

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Cited By

Braik AKoliou M(2024)Automated building damage assessment and large‐scale mapping by integrating satellite imagery, GIS, and deep learningComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1319739:15(2389-2404)Online publication date: 29-Mar-2024
https://dl.acm.org/doi/10.1111/mice.13197
Arora GR M B(2024)Disaster Scene Classification with Deep Learning: A Keras-Based Approach Utilizing Robotic Systems2024 IEEE Students Conference on Engineering and Systems (SCES)10.1109/SCES61914.2024.10652476(1-6)Online publication date: 21-Jun-2024
https://doi.org/10.1109/SCES61914.2024.10652476
Bangar AAnsari SAli SBaderao VAlhat B(2024)Disaster Prediction Using Appropriate Machine Learning Techniques2024 IEEE Pune Section International Conference (PuneCon)10.1109/PuneCon63413.2024.10895154(1-7)Online publication date: 13-Dec-2024
https://doi.org/10.1109/PuneCon63413.2024.10895154
Wu MHuang QSui TPeng BYu M(2024)A Remote Sensing Spectral Index Guided Bitemporal Residual Attention Network for Wildfire Burn Severity MappingIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.346053117(17187-17206)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3460531
Jia HYang WWang LLi H(2024)Uncertainty-Guided Segmentation Network for Geospatial Object SegmentationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.336169317(5824-5833)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2024.3361693
R HN P(2024)A Comprehensive Study on Deep Learning Techniques used for SAR and Optical Image Registration2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA)10.1109/ICAIQSA64000.2024.10882455(1-6)Online publication date: 20-Dec-2024
https://doi.org/10.1109/ICAIQSA64000.2024.10882455
Rombado LOrescanin MOrescanin M(2024)Uncertainty-Aware Aerial Coastal Imagery Pattern Recognition Through Transfer Learning With ImageNet-1K Variational EmbeddingsIEEE Access10.1109/ACCESS.2024.345137312(130866-130883)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3451373
Jeba SAurpa TAdib M(2024)From facebook posts to news headlines: using transformer models to predict post-disaster impact on mass media contentSocial Network Analysis and Mining10.1007/s13278-024-01363-114:1Online publication date: 7-Oct-2024
https://doi.org/10.1007/s13278-024-01363-1
Ramesh APrasad SSrikanth STripathi S(2023)Hurricane Damage Detection using Computer VisionProceedings of the 2023 5th International Conference on Image, Video and Signal Processing10.1145/3591156.3591174(126-132)Online publication date: 24-Mar-2023
https://dl.acm.org/doi/10.1145/3591156.3591174
Uddin RTran TCao GJo K(2023)DenseNetx: Efficient DenseNets for Remote Scene Classification without Pretraining2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)10.1109/ISIE51358.2023.10228170(1-6)Online publication date: 19-Jun-2023
https://doi.org/10.1109/ISIE51358.2023.10228170

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