LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas
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 a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/7/450 |
| _version_ | 1797562138063011840 |
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| author | Zhen Ye Yusheng Xu Rong Huang Xiaohua Tong Xin Li Xiangfeng Liu Kuifeng Luan Ludwig Hoegner Uwe Stilla |
| author_facet | Zhen Ye Yusheng Xu Rong Huang Xiaohua Tong Xin Li Xiangfeng Liu Kuifeng Luan Ludwig Hoegner Uwe Stilla |
| author_sort | Zhen Ye |
| collection | DOAJ |
| description | 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 km<sup>2</sup> 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. |
| first_indexed | 2024-03-10T18:24:15Z |
| format | Article |
| id | doaj.art-f640982175fd4c07abecff38598191d2 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T18:24:15Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-f640982175fd4c07abecff38598191d22023-11-20T07:09:51ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-07-019745010.3390/ijgi9070450LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban AreasZhen Ye0Yusheng Xu1Rong Huang2Xiaohua Tong3Xin Li4Xiangfeng Liu5Kuifeng Luan6Ludwig Hoegner7Uwe Stilla8Photogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaNational Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geo-informatics, Tongji University, Shanghai 200092, ChinaPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyThe 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 km<sup>2</sup> 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.https://www.mdpi.com/2220-9964/9/7/450ALS point cloudssemantic labelinghighly-dense urban areabenchmark dataset |
| spellingShingle | Zhen Ye Yusheng Xu Rong Huang Xiaohua Tong Xin Li Xiangfeng Liu Kuifeng Luan Ludwig Hoegner Uwe Stilla LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas ISPRS International Journal of Geo-Information ALS point clouds semantic labeling highly-dense urban area benchmark dataset |
| title | LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas |
| title_full | LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas |
| title_fullStr | LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas |
| title_full_unstemmed | LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas |
| title_short | LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas |
| title_sort | lasdu a large scale aerial lidar dataset for semantic labeling in dense urban areas |
| topic | ALS point clouds semantic labeling highly-dense urban area benchmark dataset |
| url | https://www.mdpi.com/2220-9964/9/7/450 |
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