The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can b...
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doaj-b1b693954e164450b57c6ad930808a012021-07-01T00:38:50ZengMDPI AGRemote Sensing2072-42922021-06-01132407240710.3390/rs13122407The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban AreasLeyang Zhao0Li Yan1Xiaolin Meng2School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaNottingham Geospatial Institution, University of Nottingham, Nottingham NG7 2RD, UKThe demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%.https://www.mdpi.com/2072-4292/13/12/2407LiDARstreet curbsfeature extractionpoint cloud data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Leyang Zhao Li Yan Xiaolin Meng |
spellingShingle |
Leyang Zhao Li Yan Xiaolin Meng The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas Remote Sensing LiDAR street curbs feature extraction point cloud data |
author_facet |
Leyang Zhao Li Yan Xiaolin Meng |
author_sort |
Leyang Zhao |
title |
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas |
title_short |
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas |
title_full |
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas |
title_fullStr |
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas |
title_full_unstemmed |
The Extraction of Street Curbs from Mobile Laser Scanning Data in Urban Areas |
title_sort |
extraction of street curbs from mobile laser scanning data in urban areas |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-06-01 |
description |
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%. |
topic |
LiDAR street curbs feature extraction point cloud data |
url |
https://www.mdpi.com/2072-4292/13/12/2407 |
work_keys_str_mv |
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