A minimum cover approach for extracting the road network from airborne LIDAR data

Zhu, Qihui; Mordohai, Philippos

doi:10.1109/iccvw.2009.5457423

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…To the same end, Hui et al (2016) employed skewness balancing (for intensity-based filtering), a rotating neighborhood (to remove narrow roads), and hierarchical fusion and optimization (for elimination of parking lots and bare earth). Instead of extracting only the road centerline, Zhu and Mordohai (2009) extracted the whole road network by first detecting multiple, horizontally close, ground planes, and subsequently projecting ground points intensities onto to 2D images. They extracted road features on that image using boundary and interior features and then generated hypotheses from those features.…”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of road features in urban environments using dense ALS data

Soilán

Truong-Hong

Riveiro

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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This paper describes a methodology that automatically extracts semantic information from urban ALS data for urban parameterization and road network definition. First, building façades are segmented from the ground surface by combining knowledge-based information with both voxel and raster data. Next, heuristic rules and unsupervised learning are applied to the ground surface data to distinguish sidewalk and pavement points as a means for curb detection. Then radiometric information was employed for road marking extraction. Using high-density ALS data from Dublin, Ireland, this fully automatic workflow was able to generate a F-score close to 95% for pavement and sidewalk identification with a resolution of 20cm and better than 80% for road marking detection.

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Section: Introductionmentioning

confidence: 99%

Automatic extraction of road features in urban environments using dense ALS data

Soilán

Truong-Hong

Riveiro

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Road detection from dense lidar data based on local and global information

Yong

et al. 2015

2015 23rd International Conference on Geoinformatics

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This paper proposes a novel algorithm of road detection from dense Light Detection and Ranging (LiDAR) data based on the local and global information of point clouds. First, the ground points and non-ground points are separated by a filtering algorithm. Then, road candidates are identified in the ground points by segmentation based on local intensity distribution histogram, which can make use of the homogeneity and consistency of roads. Finally, the ultimate road points are verified by global inference based on the area of the road candidate point sets, which can remove small data sets. The practical data in complex environment is utilized to test this algorithm. The experimental results show that this algorithm is capable of detecting roads automatically and efficiently, which has high robustness for complex roads and environments.

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

Zhang

et al. 2022

Remote Sensing

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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.

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A minimum cover approach for extracting the road network from airborne LIDAR data

Cited by 4 publications

References 24 publications

Automatic extraction of road features in urban environments using dense ALS data

Automatic extraction of road features in urban environments using dense ALS data

Road detection from dense lidar data based on local and global information

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

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