Recognizing basic structures from mobile laser scanning data for road inventory studies

Pu, Shi; Rutzinger, Martin; Vosselman, G.; Elberink, Sander Oude

doi:10.1016/j.isprsjprs.2011.08.006

Cited by 291 publications

(252 citation statements)

References 18 publications

Supporting

Mentioning

241

Contrasting

Order By: Relevance

“…Thus, the case study areas used in our analysis represent the typical street scene in the urban area of Shanghai. For urban street trees with more complex structures, some pre-processing steps for original laser scanning point cloud, such as partitioning of data along road direction [44], removing the points that are far away from the survey trajectory, would simplify and improve the detection and quantification of street trees.…”

Section: Tuning Of the Algorithm Parametersmentioning

confidence: 99%

“…Rutzinger et al [43] developed a method for tree recognition, in which the MLS point cloud is first segmented into planar regions using a 3D Hough transform and surface growing algorithm, and then the segments forming an individual tree are identified. Pu et al [44] adopted a percentile based pole recognition algorithm for segmenting tree trunks and crowns from MLS data.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

et al. 2013

View full text Add to dashboard Cite

Abstract:As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field OPEN ACCESS Remote Sens. 2013, 5 585 measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data.

show abstract

Section: Tuning Of the Algorithm Parametersmentioning

confidence: 99%

mentioning

confidence: 99%

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Lam et al (2010) extracted roads through fitting a plane to 3D terrestrial mobile point cloud data and then used the extracted information to distinguish lamp posts, power line posts and power lines by employing context based constraints. Pu et al (2011) segmented MLS data into traffic signs, poles, barriers, trees and building walls based on spatial characteristics of point cloud segments like size, shape, orientation and topological relationships. Similarly, Zhou and Vosselman (2012) used elevation attribute, while McElhinney et al (2010) and Kumar et al (2013) employed elevation, intensity and pulse width attributes to extract road edges in multiple route corridor environment from MLS data.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

105

View full text Add to dashboard Cite

a b s t r a c tRoad markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.

show abstract

“…For example, ALS data-sets have become an important source for object extraction and reconstruction for various applications, such as urban analysis (the roofs of buildings) [2][3][4]; vegetation analysis [5]; landform mapping [6]; DTM generation [7,8] and forest inventory [9][10][11]. However MLS data-sets are not only including the application of vegetation analysis [12][13][14], but best for detecting objects of urban areas, e.g., walls of building and collecting even more information from road surface [15], In the case of urban areas the detection and quantification of road surface is important for the implementation of urban areas solutions during the regeneration and transformation of cities. On the other hand urban road surface models are needed for accurate three-dimensional mapping of urban areas.…”

Section: Introductionmentioning

confidence: 99%

3D road surface reconstruction based on point clouds data assimilation algorithm

Chen

Lü

2017

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. Urban areas 3D model reconstruction is one of the major fields of application of 3D scanning technologies. In the future, vehicle-based laser scanning, here called mobile laser scanning system, should see considerable use for 3D road environment modelling in urban areas. In this context, one of the main limitations perceived by the mobile laser scanning system is the incompleteness of the sampling. Whenever we scan urban area road environment, the produced sampling usually presents a large number of missing regions. Many algorithmic solutions exist to close those gaps from specific hole filling algorithms to the drastic solution of using water-tight reconstruction methods. In this paper, a method for filling holes of road surface point clouds and generating 3D model of road surface from mobile laser scanning data is developed. The data is classified into road surface, on-road and off-road surface point clouds. Many large holes in the road surface point clouds are filled by using data assimilation algorithm. Then, the road surface is 3D modeled as a triangulated irregular network. It is shown that the whole road surface 3D model is integrated after data processing. The above mentioned steps are applied to a large set of mobile laser scanning data of urban area road environment, in order to obtain the whole urban road surface 3D model.

show abstract

Recognizing basic structures from mobile laser scanning data for road inventory studies

Cited by 291 publications

References 18 publications

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data

Automated road markings extraction from mobile laser scanning data

3D road surface reconstruction based on point clouds data assimilation algorithm

Contact Info

Product

Resources

About