HRHD-HK: A Benchmark Dataset of High-Rise and High-Density Urban Scenes for 3D Semantic Segmentation of Photogrammetric Point Clouds

This is the official repository of the HRHD-HK dataset. For technical details, please refer to:

Li, M., Wu, Y., Yeh, A. G. O., & Xue, F. (2023). HRHD-HK: A benchmark dataset of high-rise and high-density urban scenes for 3D semantic segmentation of photogrammetric point cloud. Proceedings of 2023 IEEE International Conference on Image Processing Challenges and Workshops, 3714-3718. IEEE. https://doi.org/10.1109/ICIPC59416.2023.10328383

This study was supported in part by the Hong Kong Research Grant Council (RGC) (27200520) and the Department of Science and Technology of Guangdong Province (GDST) (2020B1212030009, 2023A1515010757).

Overview of HRHD-HK

This paper presents a benchmark dataset of high-rise high-density urban point clouds, namely High-Rise, High-Density urban scenes of Hong Kong (HRHD-HK) for 3D semantic segmentation.

• The semantic labels of HRHD-HK include 1) building, 2) vegetation, 3) road, 4) waterbody, 5) facility, 6) terrain, and 7) vehicle.
• Point clouds of HRHD-HK were collected in HK with two features, i.e., color and coordinates in the HK 1980 Grid system (EPSG:2326).
• HRHD-HK arranged in 150 tiles, contains approximately 273 million points, covering 9.375 km².
• Each tile of point clouds was saved in the "ply" format with seven channels, i.e., x, y, z, red, green, blue, and label.
• HRHD-HK aims to supplement the existing benchmark datasets with Asian HRHD urban scenes as well as subtropical natural landscapes, such as sea, vegetation, and mountains.

HRHD-HK is now available at the HKU Data Repository.

Li, Maosu; Wu, Yijie; Yeh, Anthony Gar On; Xue, Fan (2023). Supporting data for "HRHD-HK: A Benchmark Dataset of High-Rise and High-Density Urban Scenes for 3D Semantic Segmentation of Photogrammetric Point Clouds". HKU Data Repository. Dataset. https://doi.org/10.25442/hku.23701866.v2

Please cite our paper and dataset, if you find our work useful for your research and practices. Many thanks.

For any inquiries, please feel free to contact Maosu at maosulee@connect.hku.hk or Dr. Frank at xuef@hku.hk.