Semantic Labelling of Road Furniture in Mobile Laser Scanning
Data

Li, Fashuai; Elberink, Sander Oude; Vosselman, G.

doi:10.5194/isprs-archives-xlii-2-w7-247-2017

Cited by 10 publications

(12 citation statements)

References 33 publications

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“…To examine the shape of each section, Yadav, et al [ 106 ] remove the false positive detection of pole-like objects by computing the compactness. Some methods compare the differences in diameters of adjacent sections of a pole-like object candidate against a given threshold (Li, et al [ 101 ], Lehtomäki, et al [ 108 ], Fukano and Masuda [ 113 ], Li, et al [ 115 ]).…”

Section: Object Recognitionmentioning

confidence: 99%

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Che

Jung

2019

Sensors

198

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Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

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Section: Object Recognitionmentioning

confidence: 99%

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Che

Jung

2019

Sensors

198

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“…However, additional features, such as colours and contextual features, were still needed to improve the detection accuracy of the segmentation and detection of road poles. Li et al proposed an interesting framework to decompose road furniture into different components based on their spatial relations [20][21][22]. The poles are categorized to three classes and each class of pole is extracted using three different methods.…”

Section: Studies On the Recognition Of Pole Structures In Point Cloudsmentioning

confidence: 99%

Localization and Extraction of Road Poles in Urban Areas from Mobile Laser Scanning Data

Wang

Tang

et al. 2019

Remote Sensing

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Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.

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“…(Lehtomäki et al, 2015) developed a workflow to classify roadside objects including the removal of the ground and buildings through segmentation, classification, and object location estimation and achieve 88% accuracy. Li et al (2017) proposed a novel method to recognize road furniture using their logical relations and functionalities. Their work achieved a strong performance in the interpretation of road furniture resulting in an accuracy in identifying 93.3% of poles, 94.3% of street light heads, and an overall accuracy of 76.9% was reported.…”

Section: Introductionmentioning

confidence: 99%

Classification of Mobile Terrestrial Lidar Point Cloud in Urban Area Using Local Descriptors

Zaboli

Rastiveis

Shams

et al. 2019

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

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Abstract. Automated analysis of three-dimensional (3D) point clouds has become a boon in Photogrammetry, Remote Sensing, Computer Vision, and Robotics. The aim of this paper is to compare classifying algorithms tested on an urban area point cloud acquired by a Mobile Terrestrial Laser Scanning (MTLS) system. The algorithms were tested based on local geometrical and radiometric descriptors. In this study, local descriptors such as linearity, planarity, intensity, etc. are initially extracted for each point by observing their neighbor points. These features are then imported to a classification algorithm to automatically label each point. Here, five powerful classification algorithms including k-Nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Neural Network, and Random Forest (RF) are tested. Eight semantic classes are considered for each method in an equal condition. The best overall accuracy of 90% was achieved with the RF algorithm. The results proved the reliability of the applied descriptors and RF classifier for MTLS point cloud classification.

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Semantic Labelling of Road Furniture in Mobile Laser Scanning Data

Cited by 10 publications

References 33 publications

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Localization and Extraction of Road Poles in Urban Areas from Mobile Laser Scanning Data

Classification of Mobile Terrestrial Lidar Point Cloud in Urban Area Using Local Descriptors

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