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LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas

Authors :
Zhen Ye
Yusheng Xu
Rong Huang
Xiaohua Tong
Xin Li
Xiangfeng Liu
Kuifeng Luan
Ludwig Hoegner
Uwe Stilla
Source :
ISPRS International Journal of Geo-Information, Vol 9, Iss 7, p 450 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this task with a possible solution using 3D point clouds, which are accessible, affordable, accurate, and applicable. Among all types of platforms, the airborne platform with LiDAR can serve as an efficient and effective tool for large-scale 3D mapping in the urban area. Against this background, a large number of algorithms and methods have been developed to fully explore the potential of 3D point clouds. However, the creation of publicly accessible large-scale annotated datasets, which are critical for assessing the performance of the developed algorithms and methods, is still at an early age. In this work, we present a large-scale aerial LiDAR point cloud dataset acquired in a highly-dense and complex urban area for the evaluation of semantic labeling methods. This dataset covers an urban area with highly-dense buildings of approximately 1 km2 and includes more than three million points with five classes of objects labeled. Moreover, experiments are carried out with the results from several baseline methods, demonstrating the feasibility and capability of the dataset serving as a benchmark for assessing semantic labeling methods.

Details

Language :
English
ISSN :
22209964
Volume :
9
Issue :
7
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.f640982175fd4c07abecff38598191d2
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi9070450