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UAV-Based Visual Remote Sensing for Automated Building Inspection

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13807)


Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component’s contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building plan area, objects on the rooftop and rooftop layout. The accuracy of the proposed methodology in estimating the above-mentioned parameters is verified through field measurements taken using a distance measuring sensor and also from the data obtained through Google Earth. Additional details and code can be accessed from


  • Building inspection
  • UAV-based remote sensing
  • Segmentation
  • Image stitching
  • 3D reconstruction

K. Srivastava and D. Patel—denotes equal contribution.

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  1. 1.

    UAV specification details can be found at the official DJI website:

  2. 2.

    The ToF sensor can be found at:


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The authors acknowledge the financial support provided by IHUB, IIIT Hyderabad to carry out this research work under the project: IIIT-H/IHub/Project/Mobility/2021-22/M2-003.

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Correspondence to Kushagra Srivastava .

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Srivastava, K. et al. (2023). UAV-Based Visual Remote Sensing for Automated Building Inspection. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13807. Springer, Cham.

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