Weakly-Supervised Semantic Segmentation of Airborne LiDAR Point Clouds in Hong Kong Urban Areas
- Resource Type
- Conference
- Authors
- Li, Qiaosi; Zhao, Qunshan
- Source
- 2023 Joint Urban Remote Sensing Event (JURSE) Urban Remote Sensing Event (JURSE), 2023 Joint. :1-4 May, 2023
- Subject
- Computing and Processing
Geoscience
Signal Processing and Analysis
Point cloud compression
Solid modeling
Laser radar
Three-dimensional displays
Annotations
Semantic segmentation
Atmospheric modeling
Airborne LiDAR
Point cloud classification
Urban buildings and trees
Deep learning
- Language
- ISSN
- 2642-9535
Semantic segmentation of airborne LiDAR point clouds of urban areas is an essential process prior to applying LiDAR data to further applications such as 3D city modeling. Large-scale point cloud semantic segmentation is challenging in practical applications due to the massive data size and time-consuming point-wise annotation. This paper applied weakly-supervised Semantic Query Network and sparse points annotation pipeline to practical airborne LiDAR datasets for urban scene semantic segmentation in Hong Kong. The experiment result obtained the overall accuracy over 84% and the mean intersect over union over 75%. The capacity of intensity and return attributes of LiDAR data to classify the vegetation and construction was explored and discussed. This work demonstrates an efficient workflow of large-scale airborne LiDAR point cloud semantic segmentation in practice.