Hong Kong Annotated Airborne LiDAR Point Clouds
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Description
The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds [1]. The dataset covers 16 tiles of airborne LiDAR data in an area of 7.2 km2 in Shatin, Hong Kong, China. 11 tiles were used for training, while 5 tiles were used for validation. There are multiple types of construction in the dataset including high-rise residential buildings, low-rise village houses, and large public buildings. Green spaces are mainly composed of wood areas in open spaces (e.g., in parks and hills) and planted trees in residential gardens and nearby roads. Point clouds are classified in ground, buildings, and trees.
The LiDAR data is owned by the Hong Kong government. Please visit the Spatial Data Portal, Survey Division, CEDD (https://sdportal.cedd.gov.hk/#/en/) for more details.
Files
input_0.320.zip
Files
(16.4 GB)
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Additional details
Related works
- Is part of
- Dataset: https://sdportal.cedd.gov.hk/#/en (URL)
Funding
- Innovate UK
- Urban Big Data ES/L011921/1
- Innovate UK
- Urban Big Data Centre ES/S007105/1
- Glasgow City Council
- UBDC GCC 3D City Modelling Project NA
Dates
- Collected
-
2020
Software
- Repository URL
- https://github.com/QiaosiLi/SQN_ALS_Classification
- Programming language
- Python
References
- [1] Q. Hu et al., "SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds", Computer Vision–ECCV 2022: 17th European Conference Tel Aviv Israel October 23–27 2022 Proceedings Part XXVII, pp. 600-619, Apr. 2021.