Extracting Urban Road Footprints from Airborne LiDAR Point Clouds with PointNet++ and Two-Step Post-Processing

In this paper, a novel framework for the automatic extraction of road footprints from airborne LiDAR point clouds in urban areas is proposed. The extraction process consisted of three phases: The first phase is to extract road points by using the deep learning model PointNet++, where the features of...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-02, Vol.14 (3), p.789
Main Authors: Ma, Haichi, Ma, Hongchao, Zhang, Liang, Liu, Ke, Luo, Wenjun
Format: Article
Language:English
Subjects:
Accuracy
airborne LiDAR
Algorithms
Classification
Clouds
Collinearity
Completeness
Deep learning
Digital imaging
geometric features
Lidar
Optimization
Remote sensing
road centerline
road footprints
Roads
Roads & highways
Triangulation
Urban areas
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Summary:In this paper, a novel framework for the automatic extraction of road footprints from airborne LiDAR point clouds in urban areas is proposed. The extraction process consisted of three phases: The first phase is to extract road points by using the deep learning model PointNet++, where the features of the input data include not only those selected from raw LiDAR points, such as 3D coordinate values, intensity, etc., but also the digital number (DN) of co-registered images and generated geometric features to describe a strip-like road. Then, the road points from PointNet++ were post-processed based on graph-cut and constrained triangulation irregular networks, where both the commission and omission errors were greatly reduced. Finally, collinearity and width similarity were proposed to estimate the connection probability of road segments, thereby improving the connectivity and completeness of the road network represented by centerlines. Experiments conducted on the Vaihingen data show that the proposed framework outperformed others in terms of completeness and correctness; in addition, some narrower residential streets with 2 m width, which have normally been neglected by previous studies, were extracted. The completeness and the correctness of the extracted road points were 84.7% and 79.7%, respectively, while the completeness and the correctness of the extracted centerlines were 97.0% and 86.3%, respectively.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14030789