Extracting roads from dense point clouds in large scale urban environment

Boyko, Aleksey; Funkhouser, Thomas

doi:10.1016/j.isprsjprs.2011.09.009

Cited by 151 publications

(72 citation statements)

References 24 publications

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“…(iv) Road modeling Studies of road extraction usually use images ( [23][24][25]), ALS ( [26][27][28]) or both ( [29,30]) as data sources. Currently, certain methods based on MLS data are also available: Goulette et al [31], Kukko [32], Jaakkola et al [33] and Pu et al [21].…”

Section: (I) Building Extraction and Reconstructionmentioning

confidence: 99%

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

Zhu

Hyyppä

2014

Remote Sensing

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This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution.

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Section: (I) Building Extraction and Reconstructionmentioning

confidence: 99%

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

Zhu

Hyyppä

2014

Remote Sensing

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“…Misdetections and over-detections on the street surface are evaluated in Bin et al [30], however, they are calculated based on the detection of points, rather than based on the surfaces. Completeness and correctness are reported in Boyko and Funkhouser [26] in a similar way to this study, although a 0.5 m × 0.5 m grid is used for surface comparisons. Completeness is 94%, and correctness is 86%.…”

mentioning

confidence: 91%

“…In contrast, Zhao et al [28], Smadja et al [29] and Guan et al [35] show methods for automatically identifying road points from MLS data, but their performance is not assessed. Boyko and Funkhouser, and Bin et al [26,30] are focused on the detection of the road surface, but limited to the presence of curbs. Misdetections and over-detections on the street surface are evaluated in Bin et al [30], however, they are calculated based on the detection of points, rather than based on the surfaces.…”

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confidence: 99%

An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

et al. 2016

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Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping from point cloud laser data. Using geometric criteria, the initial 3D LiDAR point data is structured in lines covering the road surface. These lines are then grouped according to a set of quasi-planar restriction rules. Road asphalt edge limits are extracted from the end points of lines belonging to these groups. Finally a two-stage smoothing procedure is applied to correct for edge occlusions and other anomalies. The method was tested on a 2.1 km stretch of road, and the results were checked using a RTK-GNSS measured dataset as ground truth. Correctness and completeness were 99% and 97%, respectively.

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Section: Accuracy Evaluationmentioning

confidence: 99%

“…The manually delineated roads are shown in Figure 7, which presents the extracted roads for comparison. The following three widely accepted evaluation measures are used to evaluate how well our road extraction results match the ground-truth data set [4,5,[28][29][30]. The following three widely accepted evaluation measures are used to evaluate how well our road extraction results match the ground-truth data set [4,5,[28][29][30].…”

Section: Accuracy Evaluationmentioning

confidence: 99%

A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

Wang

Qin

Gao³

et al. 2016

IJGI

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Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran's I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.

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Extracting roads from dense point clouds in large scale urban environment

Cited by 151 publications

References 24 publications

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

The Use of Airborne and Mobile Laser Scanning for Modeling Railway Environments in 3D

An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

A New Approach to Urban Road Extraction Using High-Resolution Aerial Image

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