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Challenges in building extraction from airborne LiDAR data: ground-truth, building boundaries, and evaluation metrics

Authors:

Hunsoo Song,

Jinha JungAuthors Info & Claims

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

Article No.: 118, Pages 1 - 4

https://doi.org/10.1145/3557915.3565983

Published: 22 November 2022 Publication History

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Abstract

2D and 3D building maps provide essential information for understanding urbanization and diverse geospatial applications. Airborne laser scanning (ALS) is known to be an effective method for 2D and 3D building mappings. Despite numerous efforts to develop automated and accurate building extraction algorithms for ALS, several challenges remain for reliable large-area building mappings. The 30^th ACM SIGSPATIAL 2022 held a competition (GISCUP 2022) for large-area building mapping using ALS data. This paper illustrates the implementation of the algorithm that won first place at GISCUP 2022. In addition, we describe some critical issues with large-area building mappings and evaluation methods. Specifically, issues related to ground-truth, building boundaries, and the selection of evaluation metrics (e.g. Intersection over Union) were discussed.

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Cited By

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Sulaiman MFarmanbar MBelbachir ARong C(2024)Genetic Algorithm Empowering Unsupervised Learning for Optimizing Building Segmentation from Light Detection and Ranging Point CloudsRemote Sensing10.3390/rs1619360316:19(3603)Online publication date: 27-Sep-2024
https://doi.org/10.3390/rs16193603
Song HJung J(2024)An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR DataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.332977317(6067-6084)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2023.3329773
Song HJung J(2023)An Object-Based Ground Filtering of Airborne LiDAR Data for Large-Area DTM GenerationRemote Sensing10.3390/rs1516410515:16(4105)Online publication date: 21-Aug-2023
https://doi.org/10.3390/rs15164105
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Index Terms

Challenges in building extraction from airborne LiDAR data: ground-truth, building boundaries, and evaluation metrics
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
      2. Image and video acquisition
        3D imaging

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Information & Contributors

Information

Published In

SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems

November 2022

806 pages

ISBN:9781450395298

DOI:10.1145/3557915

General Chairs:
Matthias Renz
Kiel University, Germany
,
Mohamed Sarwat
Wherobots Inc. / Arizona State University

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 November 2022

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Short-paper

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SIGSPATIAL '22

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SIGSPATIAL

SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems

November 1 - 4, 2022

Washington, Seattle

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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Sulaiman MFarmanbar MBelbachir ARong C(2024)Genetic Algorithm Empowering Unsupervised Learning for Optimizing Building Segmentation from Light Detection and Ranging Point CloudsRemote Sensing10.3390/rs1619360316:19(3603)Online publication date: 27-Sep-2024
https://doi.org/10.3390/rs16193603
Song HJung J(2024)An Unsupervised, Open-Source Workflow for 2D and 3D Building Mapping From Airborne LiDAR DataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.332977317(6067-6084)Online publication date: 2024
https://doi.org/10.1109/JSTARS.2023.3329773
Song HJung J(2023)An Object-Based Ground Filtering of Airborne LiDAR Data for Large-Area DTM GenerationRemote Sensing10.3390/rs1516410515:16(4105)Online publication date: 21-Aug-2023
https://doi.org/10.3390/rs15164105
Brooks CIsci SKanza YKlosowski JWoods R(2023)ACM SIGSPATIAL GISCUP 2022 Workshop Report: Extracting Building Footprints from LiDAR Point Clouds Seattle, Washington, USA, November 1, 2022SIGSPATIAL Special10.1145/3632268.363228514:1(51-55)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3632268.3632285
Song HCervini GJung J(2023)Assessment of Local Climate Zone Products Via Simplified Classification Rule with 3D Building MapsIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS52108.2023.10283361(3679-3682)Online publication date: 16-Jul-2023
https://doi.org/10.1109/IGARSS52108.2023.10283361

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Automatic Building Detection Using Airborne LIDAR Data

3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database

Urban building roof segmentation from airborne lidar point clouds