Published August 27, 2024 | Version v1
Dataset Open

Hong Kong Annotated Airborne LiDAR Point Clouds

Authors/Creators

Contributors

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

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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

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.