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

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...

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Bibliographic Details
Published in:Applied sciences 2022-02, Vol.12 (4), p.1975
Main Authors: Shin, Young-Ha, Son, Kyung-Wahn, Lee, Dong-Cheon
Format: Article
Language:English
Subjects:
Accuracy
Boundaries
Buildings
Classification
Clouds
Datasets
Deep learning
Global positioning systems
GPS
Lasers
LiDAR data
multiple return
Neural networks
Photogrammetry
point clouds
Remote sensing
Semantic segmentation
Semantics
Spatial data
Vegetation
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12041975