Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++

Applied Sciences

Kyung-Wahn Son¹  Dong-Cheon Lee¹  Young-Ha Shin² 

[1] Department of Environment, Energy & Geoinformatics, Sejong University, Seoul 05006, Korea;Department of Geoinformation Engineering, Sejong University, Seoul 05006, Korea;

关键词: LiDAR data;    point clouds;    multiple return;    deep learning;    semantic segmentation;   

DOI  :  10.3390/app12041975

来源: DOAJ

【 摘 要 】

Light detection and ranging (LiDAR) data of 3D point clouds acquired from laser sensors is a crucial form of geospatial data for recognition of complex objects since LiDAR data provides geometric information in terms of 3D coordinates with additional attributes such as intensity and multiple returns. In this paper, we focused on utilizing multiple returns in the training data for semantic segmentation, in particular building extraction using PointNet++. PointNet++ is known as one of the efficient and robust deep learning (DL) models for processing 3D point clouds. On most building boundaries, two returns of the laser pulse occur. The experimental results demonstrated that the proposed approach could improve building extraction by adding two returns to the training datasets. Specifically, the recall value of the predicted building boundaries for the test data was improved from 0.7417 to 0.7948 for the best case. However, no significant improvement was achieved for the new data because the new data had relatively lower point density compared to the training and test data.

【 授权许可】

Unknown