A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data
Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this wo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5892 |
| _version_ | 1797464026948567040 |
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| author | Xiao Li Fang Qiu Fan Shi Yunwei Tang |
| author_facet | Xiao Li Fang Qiu Fan Shi Yunwei Tang |
| author_sort | Xiao Li |
| collection | DOAJ |
| description | Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics. |
| first_indexed | 2024-03-09T18:02:08Z |
| format | Article |
| id | doaj.art-8812a09241904a24a12aa8d2fcc9f9b4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:08Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8812a09241904a24a12aa8d2fcc9f9b42023-11-24T09:52:08ZengMDPI AGRemote Sensing2072-42922022-11-011422589210.3390/rs14225892A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR DataXiao Li0Fang Qiu1Fan Shi2Yunwei Tang3Department of Geospatial Information Sciences, The University of Texas at Dallas, Richardson, TX 75080, USADepartment of Geospatial Information Sciences, The University of Texas at Dallas, Richardson, TX 75080, USACollege of Information Science and Engineering, Henan University of Technology, No. 100 Lianhua Road, Zhengzhou 450001, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaAutomatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics.https://www.mdpi.com/2072-4292/14/22/5892airborne LiDARbuilding footprintsignal-based regularization |
| spellingShingle | Xiao Li Fang Qiu Fan Shi Yunwei Tang A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data Remote Sensing airborne LiDAR building footprint signal-based regularization |
| title | A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data |
| title_full | A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data |
| title_fullStr | A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data |
| title_full_unstemmed | A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data |
| title_short | A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data |
| title_sort | recursive hull and signal based building footprint generation from airborne lidar data |
| topic | airborne LiDAR building footprint signal-based regularization |
| url | https://www.mdpi.com/2072-4292/14/22/5892 |
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