scholarly journals Automated Localization and Classification of Expressway Pole-Like Road Facilities from Mobile Laser Scanning Data

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Tran Thanh Ha ◽  
Taweep Chaisomphob
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Advanced Technology ◽  
Promising Alternative ◽  
Knowledge Based ◽  
Road Mapping ◽  
Vertical Height

Mobile LiDAR is an emerging advanced technology for capturing three-dimensional road information at a large scale effectively and precisely. Pole-like road facilities are crucial street infrastructures as they provide valuable information for road mapping and road inventory. Thus, the automated localization and classification of road facilities are necessary. This paper proposes a voxel-based method to detect and classify pole-like objects in an expressway environment based on the spatially independent and vertical height continuity analysis. First, the ground points are eliminated, and the nonground points are merged into clusters. Second, the pole-like objects are extracted using horizontal cross section analysis and minimum vertical height criteria. Finally, a set of knowledge-based rules, which comprise height features and geometric shape, is constructed to classify the detected road poles into different types of road facilities. Two test sites of point clouds in an expressway environment, which are located in Bangkok, Thailand, are used to assess the proposed method. The proposed method extracts the pole-like road facilities from two datasets with a detection rate of 95.1% and 93.5% and an overall quality of 89.7% and 98.0% in the classification stage, respectively. This shows that the algorithm could be a promising alternative for the localization and classification of pole-like road facilities with acceptable accuracy.

scholarly journals ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

scholarly journals Weighing trees with lasers: advances, challenges and opportunities

2018 ◽  
Vol 8 (2) ◽  
pp. 20170048 ◽  
Author(s):  
M. I. Disney ◽  
M. Boni Vicari ◽  
A. Burt ◽  
K. Calders ◽  
S. L. Lewis ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Tree Form ◽  
Form And Function ◽  
Challenges And Opportunities ◽  
And Function ◽  
Non Destructive

Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

scholarly journals Transparent Collision Visualization of Point Clouds Acquired by Laser Scanning

2019 ◽  
Vol 8 (9) ◽  
pp. 425
Author(s):  
Weite Li ◽  
Kenya Shigeta ◽  
Kyoko Hasegawa ◽  
Liang Li ◽  
Keiji Yano ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
City Government ◽  
Three Dimensional Objects ◽  
Kyoto City ◽  
Risk Areas ◽  
Scale Point ◽  
The City

In this paper, we propose a method to visualize large-scale colliding point clouds by highlighting their collision areas, and apply the method to visualization of collision simulation. Our method uses our recent work that achieved precise three-dimensional see-through imaging, i.e., transparent visualization, of large-scale point clouds that were acquired via laser scanning of three-dimensional objects. We apply the proposed collision visualization method to two applications: (1) The revival of the festival float procession of the Gion Festival, Kyoto city, Japan. The city government plans to revive the original procession route, which is narrow and not used at present. For the revival, it is important to know whether the festival floats would collide with houses, billboards, electric wires, or other objects along the original route. (2) Plant simulations based on laser-scanned datasets of existing and new facilities. The advantageous features of our method are the following: (1) A transparent visualization with a correct depth feel that is helpful to robustly determine the collision areas; (2) the ability to visualize high collision risk areas and real collision areas; and (3) the ability to highlight target visualized areas by increasing the corresponding point densities.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

scholarly journals Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4583 ◽  
Author(s):  
Xiaoqiang Liu ◽  
Yanming Chen ◽  
Shuyi Li ◽  
Liang Cheng ◽  
Manchun Li
Keyword(s):  
Supervised Learning ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Hierarchical Classification ◽  
Training Data ◽  
Classification Model ◽  
Learning Method ◽  
Intensity Information

Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information.

scholarly journals Large-Scale ALS Data Semantic Classification Integrating Location-Context-Semantics Cues by Higher-Order CRF

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1700
Author(s):  
Wei Han ◽  
Ruisheng Wang ◽  
Daqing Huang ◽  
Cheng Xu
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Point Clouds ◽  
Higher Order ◽  
Semantic Classification ◽  
Benchmark Datasets ◽  
Media Lab

We designed a location-context-semantics-based conditional random field (LCS-CRF) framework for the semantic classification of airborne laser scanning (ALS) point clouds. For ALS datasets of high spatial resolution but with severe noise pollutions, more contexture and semantics cues, besides location information, can be exploited to surmount the decrease of discrimination of features for classification. This paper mainly focuses on the semantic classification of ALS data using mixed location-context-semantics cues, which are integrated into a higher-order CRF framework by modeling the probabilistic potentials. The location cues modeled by the unary potentials can provide basic information for discriminating the various classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between points to favor spatial smoothing. The semantics cues are explicitly encoded in the higher-order potentials. The higher-order potential operates at the clusters level with similar geometric and radiometric properties, guaranteeing the classification accuracy based on semantic rules. To demonstrate the performance of our approach, two standard benchmark datasets were utilized. Experiments show that our method achieves superior classification results with an overall accuracy of 83.1% on the Vaihingen Dataset and an overall accuracy of 94.3% on the Graphics and Media Lab (GML) Dataset A compared with other classification algorithms in the literature.

scholarly journals Fused Transparent Visualization of Point Cloud Data and Background Photographic Image for Tangible Cultural Heritage Assets

2019 ◽  
Vol 8 (8) ◽  
pp. 343 ◽  
Author(s):  
Li ◽  
Hasegawa ◽  
Nii ◽  
Tanaka
Keyword(s):  
Cultural Heritage ◽  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Photographic Image ◽  
Scale Point

Digital archiving of three-dimensional cultural heritage assets has increased the demand for visualization of large-scale point clouds of cultural heritage assets acquired by laser scanning. We proposed a fused transparent visualization method that visualizes a point cloud of a cultural heritage asset in an environment using a photographic image as the background. We also proposed lightness adjustment and color enhancement methods to deal with the reduced visibility caused by the fused visualization. We applied the proposed method to a laser-scanned point cloud of a high-valued cultural festival float with complex inner and outer structures. Experimental results demonstrate that the proposed method enables high-quality transparent visualization of the cultural asset in its surrounding environment.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

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