A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements

Jaakkola, Anttoni; Hyyppä, Juha; Kukko, Antero; Yu, Xiaowei; Kaartinen, Harri; Lehtomäki, Matti; Lin, Yi

doi:10.1016/j.isprsjprs.2010.08.002

Cited by 312 publications

(256 citation statements)

References 42 publications

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“…In comparison with field measurements, the R 2 values for the two study areas are larger than 0.9. For DBH, its R 2 value is slightly lower than for tree height, but the RMSEs for DBH are as low as 0.03 m in the first case study area, and 0.01 m in the second case study area, which are comparable to other studies (e.g., 0.021 m reported by [35]). The R 2 values for CBH in the two case studies are 0.81 and 0.82, which are better than the results reported by using ALS data (e.g., 0.49 to 0.80 reported in [46]).…”

Section: Accuracy Comparisonsupporting

confidence: 84%

“…MLS has been used for modeling road surfaces [31], extracting building facades [29,[32][33][34], and detecting trees, streetlights, and other pole-like objects [28,[35][36][37]. Previous studies have demonstrated the great potential of MLS for street objects identification.…”

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confidence: 99%

“…Lehtomä ki et al [28] proposed a scan line segmentation and cluster method to detect vertical pole-like objects in a road environment from MLS data. Based on MLS data, Jaakkola et al [35] separated trees by clustering the extracted vertical line segments and examining the spatial distribution of clusters. 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.…”

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confidence: 99%

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

et al. 2013

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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.

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Section: Accuracy Comparisonsupporting

confidence: 84%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2013

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“…A challenge is to effectively locate objects of interest, such as manhole covers, telegraph poles, building corners, and so on, from multi-view images acquainted by an MMS, for constructing and maintaining georeferenced datasets of certain objects [5,6]. An intuitive solution is to automatically recognize the designated object instances with high precision and recall rate, then calculate their geolocation by GPS/INS and triangulation according to specific points in objects (for example, the geometric center of a manhole cover or the base of a pole).…”

Section: Introductionmentioning

confidence: 99%

Template Matching for Wide-Baseline Panoramic Images from a Vehicle-Borne Multi-Camera Rig

Hong³

et al. 2018

IJGI

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Automatic detection and locating of objects such as poles, traffic signs, and building corners in street scenes captured from a mobile mapping system has many applications. Template matching is a technique that could automatically recognise the counterparts or correspondents of an object from multi-view images. In this study, we aim at finding correspondents of an object from wide baseline panoramic images with large geometric deformations from sphere projection and significant systematic errors from multi-camera rig geometry. Firstly, we deduce the camera model and epipolar model of a multi-camera rig system. Then, epipolar errors are analysed to determine the search area for pixelwise matching. A low-cost laser scanner is optionally used to constrain the depth of an object. Lastly, several classic feature descriptors are introduced to template matching and evaluated on the multi-view panoramic image dataset. We propose a template matching method combining a fast variation of a scale-invariant feature transform (SIFT) descriptor. Our method experimentally achieved the best performance in terms of accuracy and efficiency comparing to other feature descriptors and the most recent robust template matching methods.

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“…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.

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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.

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A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements

Cited by 312 publications

References 42 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

Template Matching for Wide-Baseline Panoramic Images from a Vehicle-Borne Multi-Camera Rig

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

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