LiDAR-based Building Damage Detection in Edge-Cloud Continuum

Sambit Kumar Mishra, Mohana Lasya Sanisetty, Apsareena Zulekha Shaik, Sai Likitha Thotakura, Sai Likhita Aluru, Deepak Puthal

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Scopus citations

    Abstract

    In recent years, natural disasters such as earth-quakes and hurricanes have caused significant damage to buildings and infrastructure worldwide. As a result, there has been an increasing demand for efficient and accurate methods of assessing the extent of building damage to facilitate effective recovery efforts. One emerging technology that shows great promise in this area is Light Detection and Ranging (Li-DAR). Therefore, this paper proposes a novel detection framework utilizing textural feature extraction strategies for Li-DAR-based building damage detection. Li-DAR, a remote sensing technology, has ability to create detailed maps of buildings and other infrastructure, allowing for precise identification and measurement of damage caused by natural disasters. Integration of the popular paradigm Edge cloud continuum extends cloud's capabilities to the edge of the network, enabling more effective post-disaster recovery efforts. Smart Li-DAR sensors pre-process the captured data and send it to the nearest edge device for further processing.. Inclusion of machine learning algorithms like K-means clustering algorithm here is used to classify the buildings into damaged and undamaged classes by analyzing the extracted textural features. The scheme can detect various types of building damage. The cloud server is utilized to store the processed maps. The integration of the Edge-Cloud Continuum (ECC) has added more value by reducing the network usage, and latency of the Li-DAR-based building damage detection system. ECC enables processing and analysis of data at the point of origin as well as large-scale data processing and storage in cloud-based systems. This proposed framework has shown promising results in preliminary experiments and has the potential to revolutionize post-disaster recovery efforts by providing efficient building damage maps.

    Original languageBritish English
    Title of host publication2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages252-257
    Number of pages6
    ISBN (Electronic)9798350304602
    DOIs
    StatePublished - 2023
    Event2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023 - Abu Dhabi, United Arab Emirates
    Duration: 14 Nov 202317 Nov 2023

    Publication series

    Name2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

    Conference

    Conference2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023
    Country/TerritoryUnited Arab Emirates
    CityAbu Dhabi
    Period14/11/2317/11/23

    Keywords

    • Building Damage Detection
    • Edge-cloud Continuum
    • K-means Clustering
    • Latency
    • Light Detection and Ranging (Li-DAR)
    • Network usage
    • Post disaster recovery

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