Automated road extraction from LiDAR data based on intensity and aerial photo

Gong, Li; Zhang, Yongsheng; Li, Zhengguo; Bao, Qianzong

doi:10.1109/cisp.2010.5647354

Cited by 14 publications

(7 citation statements)

References 2 publications

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“…For the purpose of this paper, we define LIDAR data point as pi, ( , , , ) i p lpx lpy lpz lpi = (1) where lpx , lpy , and lpz represent the last pulse laser strike 3D coordinates and lpi represents the intensity of the point. Let S represents the set of all laser points,…”

Section: Hierarchical Algorithmmentioning

confidence: 99%

“…Using the LiDAR(Light Detection And Ranging) technology, we can obtain the 3-D information of the earth surface quickly and accurately [1]. By contrast with traditional photogrammetry, the 3D urban data capturing using LiDAR is of higher speed, higher vertical accuracy and lower cost [2].…”

Section: Introductionmentioning

confidence: 99%

“…Such as Jeong-Heon Song makes an assessment of possibility with intensity data to classify the point cloud [3] ,and Farhad Samadzadegan use classifier fusion method [5]. Liang Gong uses K-means clustering algorithm to detect roads based on intensity return and the 3D information of LiDAR data [1]. But these methods only consider the intensity value of LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Algorithm in DTM Generation and Automatic Extraction of Road From Lidar Data

Yu-jun

Wang

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Growing demand for an efficient land use above and below the ground is motivating cadastre and land management systems to move from traditional 2D systems toward three dimensional ones. Airborne laser technology offers direct acquisition of dense and accurate 3D data. In order to get 3D road this paper proposes a hierarchical algorithm to extract terrain point from LIDAR data. We stratify the raw LiDAR data according to the height, judge terrain points and non-terrain points by the connectivity. In the case of road network, it indicates the morphological characteristics of network structure with a certain length continuous strip and small difference in intensity. All these information, including elevation information, the intensity information, the morphological characteristics and other local features, are used for extracting the road network from DTM. Local morphological filtering method is implementing for finding clear boundaries and rich details of the road profile. Following the presentation of the algorithm results for this approach are shown and evaluated

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Section: Hierarchical Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Algorithm in DTM Generation and Automatic Extraction of Road From Lidar Data

Yu-jun

Wang

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For road vs. grass and road vs. Tree, the separabilities are very high. LiDAR is better appropriate for automatic road extraction in remote sensing due to these factors [15][16][17][18]. Yuan Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Road extraction from airborne LiDAR data using SBF and CD-TIN

Narwade

Musande

2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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This paper proposes a method for automated road extraction from airborne Light Detection and Ranging (LiDAR) data. The method combines Segmentation Based Filtering (SBF) with Triangular Irregular Network-based segmentation to extract the road points. The method contains two major steps. Firstly, Segmentation Based Filtering (SBF) is applied to LiDAR data for initial segmentation of road regions. Here, region growing algorithm is applied after detecting outliers and subsequently, ground reference points are also identified using impainting with interpolation method. Secondly, road extracted by the SBF method is refined through the help of Constrained Delaunay Triangular Irregular Network based segmentation and then, road contour is extracted from road point image. For the experimental validation, the proposed and existing method is tested against ISPRS reference dataset. The experimental results showed that proposed method achieved that the completeness of the approach is 83.40 % and the correctness value is 83.02 % and the accuracy is 83.16%.

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“…The method firstly uses a clustering algorithm to divide the LiDAR intensity data into two major categories of road and non-road according to the intensity value, then the intensity data is fused with colour information in the aerial remote sensing image. The fusion result contains both the location and intensity as well as the echo time information and spectral information, the experiment result shows that the fusion method could improve the extraction accuracy (Gong et al, 2010). Samadzadegan et al proposed a road information extraction method from LiDAR data based on the fusion of multiple classifiers (MCS), the results show that the use of multi-classifier fusion method is better than using a single classifier (Samadzadegan et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A Quality Assessment Method for 3d Road Polygon Objects

Gao

Shi

Wan

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the development of the economy, the fast and accurate extraction of the city road is significant for GIS data collection and update, remote sensing images interpretation, mapping and spatial database updating etc. 3D GIS has attracted more and more attentions from academics, industries and governments with the increase of requirements for interoperability and integration of different sources of data. The quality of 3D geographic objects is very important for spatial analysis and decision-making. This paper presents a method for the quality assessment of the 3D road polygon objects which is created by integrating 2D Road Polygon data with LiDAR point cloud and other height information such as Spot Height data in Hong Kong Island. The quality of the created 3D road polygon data set is evaluated by the vertical accuracy, geometric and attribute accuracy, connectivity error, undulation error and completeness error and the final results are presented.

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Automated road extraction from LiDAR data based on intensity and aerial photo

Cited by 14 publications

References 2 publications

Hierarchical Algorithm in DTM Generation and Automatic Extraction of Road From Lidar Data

Hierarchical Algorithm in DTM Generation and Automatic Extraction of Road From Lidar Data

Road extraction from airborne LiDAR data using SBF and CD-TIN

A Quality Assessment Method for 3d Road Polygon Objects

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