Hierarchical extraction of urban objects from mobile laser scanning data

Wang, Jinhu; Zhao, Gang; Dai, Wei

doi:10.1016/j.isprsjprs.2014.10.005

Cited by 198 publications

(158 citation statements)

References 23 publications

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“…Some works aim to detect and classify a relatively large number of objects, for example Luo et al (2015) distinguish seven categories of objects including several forms of vegetation using a patch-based match graph structure. Yang et al (2015) extract urban objects (poles, cars, buildings…) segmenting a supervoxel structure and classifying the segments according to a series of heuristic rules. Serna and Marcotegui (2014) classify up to 20 different objects using Support Vector Machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

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

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ABSTRACT:The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

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Section: Introductionmentioning

confidence: 99%

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

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

View full text Add to dashboard Cite

show abstract

“…Eigenvalues are computed with every point neighbourhood. In this paper, linear, planar and volumetric features are defined as: (Yang et al, 2015) Single neighbourhood size can partly represent geometric features of points. By using a multi-scale neighbourhood the features across different scales could be included (Weinmann et al, 2015).…”

Section: Ransac Line Fitting Methodmentioning

confidence: 99%

“…Different from point-based classification, a voxelbased approach is presented to classify street furniture into different categories (Aijazi et al, 2013). Yang et al (2015) combine this with a hierarchical strategy to classify urban objects in MLS data. Bremer et al (2013) use a graph-based method with eigenvalues and multi-scale features to separate urban objects into different categories.…”

Section: Introductionmentioning

confidence: 99%

Pole-Like Street Furniture Decompostion in Mobile Laser Scanning Data

Li¹,

Elberink²,

Vosselman³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Automatic semantic interpretation of street furniture has become a popular topic in recent years. Current studies detect street furniture as connected components of points above the street level. Street furniture classification based on properties of such components suffers from large intra class variability of shapes and cannot deal with mixed classes like traffic signs attached to light poles. In this paper, we focus on the decomposition of point clouds of pole-like street furniture. A novel street furniture decomposition method is proposed, which consists of three steps: (i) acquirement of prior-knowledge, (ii) pole extraction, (iii) components separation. For the pole extraction, a novel global pole extraction approach is proposed to handle 3 different cases of street furniture. In the evaluation of results, which involves the decomposition of 27 different instances of street furniture, we demonstrate that our method decomposes mixed classes street furniture into poles and different components with respect to different functionalities.

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“…These were the only object classes considered in their experiment. Yang et al (2015) achieved a good accuracy level for the extraction of urban objects based on segmentation of super-voxels, rather than individual points, using a set of rules defined for uniting separate segments. However, the design of rules and the setting of thresholds in the identification and classification of different object classes required manual interpretation and interaction based on the shape (geometric structure), height and width information of each object.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The classification of pole-like objects is mainly achieved by setting a series of thresholds for feature values (Aijazi et al, 2013;Li and Elberink, 2013;Masuda et al, 2013;Pu and Vosselman, 2009;Yang et al, 2015;Yokoyama et al, 2013). The shortcoming of these knowledge-based methods is that the thresholds should be adjusted under different scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Step Classification Approach to Distinguishing Similar Objects in Mobile Lidar Point Clouds

Khoshelham

Fraser

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

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Hierarchical extraction of urban objects from mobile laser scanning data

Cited by 198 publications

References 23 publications

Automatic Road Sign Inventory Using Mobile Mapping Systems

Automatic Road Sign Inventory Using Mobile Mapping Systems

Pole-Like Street Furniture Decompostion in Mobile Laser Scanning Data

A Two-Step Classification Approach to Distinguishing Similar Objects in Mobile Lidar Point Clouds

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