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Laser Scanning and Point Cloud Processing in Urban Environments

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 20556

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Special Issue Editors

Dr. Sander Oude Elberink

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Guest Editor

Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
Interests: point cloud processing; object detection and classification of MLS and ALS point clouds; 3D modelling of buildings; detection and modelling of infrastructural objects; fusing point clouds with large-scale topographic map data
Special Issues, Collections and Topics in MDPI journals

Dr. Kourosh Khoshelham

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Guest Editor

Department of Infrastructure Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: photogrammetry; 3D computer vision; remote sensing; machine learning; deep learning; automated interpretation of imagery and point clouds
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cheng Wang

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Guest Editor

Department of Computer Science, School of Informatics, Xiamen University, 422 Siming Road South, Xiamen 361005, Fujian, China
Interests: 3D vision; LiDAR; mobile mapping; geospatial big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After a very successful first edition of the Special Issue on Laser Scanning and Point Cloud Classification, in the second edition, we focus on the urban environment. Accurate 3D digital representations play an important role in a wide range of urban applications. Laser scanning is the principal technology for efficient 3D data capture in the form of point clouds. Point clouds can be generated from laser scanners or derived from image matching techniques; specifically, the focus in this Special Issue is on laser scanner point clouds. However, a point is simply a point; it is the context that delivers information on the object behind the point. Research challenges in the field of laser scanning and point cloud processing range from calibration, fusion, interpretation, motion estimation, and modelling, to efficient information extraction, scene understanding, and visualization topics. The scope of this Special Issue is therefore rather broad, in the sense that we would like to include indoor, mobile, and airborne laser scanners in combination with point cloud processing algorithms for applications in the urban environment.

Dr. Sander Oude Elberink
Dr. Kourosh Khoshelham
Prof. Cheng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Point cloud
Laser scanning
Classification
Segmentation
Calibration
3D modelling
Motion and pose estimation
Change detection
Deep learning
Urban environment

Published Papers (7 papers)

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Research

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21 pages, 8574 KiB

Open AccessArticle

Adaptive Polar-Grid Gaussian-Mixture Model for Foreground Segmentation Using Roadside LiDAR

by Luyang Wang and Jinhui Lan

Remote Sens. 2022, 14(11), 2522; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112522 - 25 May 2022

Cited by 5 | Viewed by 1899

Abstract

Roadside LiDAR has become an important sensor for the detection of objects in cities, such as vehicles and pedestrians, which is due to its advantages of all-weather operation and high-ranging accuracy. In order to serve an intelligent transportation system, the efficient and accurate segmentation of vehicles and pedestrians is needed in the coverage area of the LiDAR. In this study, a roadside LiDAR was fixed on brackets on both sides of the road to obtain the point-cloud information on the urban road and the surrounding environment. A segmentation method that is based on a scanning LiDAR sensor is proposed. First, a polar grid that is based on polar coordinates is constructed to count the LiDAR rotations to obtain the original information of the angle and the distance of the point cloud, and the background point-cloud image is dynamically updated over time. By aiming at the complex urban road environment and the interference of trees and light poles in the background, an adaptive polar-grid Gaussian-mixture model (APG-GMM) that uses a point-cloud method is proposed to improve the accuracy of the foreground and background segmentation. A density-adaptive DBSCAN target-clustering algorithm is proposed, as well as a dynamic adaptive neighborhood radius, to solve the problem of the low clustering accuracy that is caused by the uneven density of point clouds that are collected by LiDAR, and to divide the point clouds in the foreground into vehicles and pedestrians. Finally, the method was tested at intersections and urban roads with dense traffic flows. The experimental results show that the proposed algorithm can segment the foreground and background well and can cluster vehicles and pedestrians while reducing the number of calculations and the time complexity. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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18 pages, 5308 KiB

Open AccessArticle

Seamless Vehicle Positioning by Lidar-GNSS Integration: Standalone and Multi-Epoch Scenarios

by Junjie Zhang, Kourosh Khoshelham and Amir Khodabandeh

Remote Sens. 2021, 13(22), 4525; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224525 - 10 Nov 2021

Cited by 13 | Viewed by 2448

Abstract

Accurate and seamless vehicle positioning is fundamental for autonomous driving tasks in urban environments, requiring the provision of high-end measuring devices. Light Detection and Ranging (lidar) sensors, together with Global Navigation Satellite Systems (GNSS) receivers, are therefore commonly found onboard modern vehicles. In this paper, we propose an integration of lidar and GNSS code measurements at the observation level via a mixed measurement model. An Extended Kalman-Filter (EKF) is implemented to capture the dynamic of the vehicle movement, and thus, to incorporate the vehicle velocity parameters into the measurement model. The lidar positioning component is realized using point cloud registration through a deep neural network, which is aided by a high definition (HD) map comprising accurately georeferenced scans of the road environments. Experiments conducted in a densely built-up environment show that, by exploiting the abundant measurements of GNSS and high accuracy of lidar, the proposed vehicle positioning approach can maintain centimeter-to meter-level accuracy for the entirety of the driving duration in urban canyons. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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28 pages, 64879 KiB

Open AccessArticle

Building Extraction from Terrestrial Laser Scanning Data with Density of Projected Points on Polar Grid and Adaptive Threshold

by Maolin Chen, Xiangjiang Liu, Xinyi Zhang, Mingwei Wang and Lidu Zhao

Remote Sens. 2021, 13(21), 4392; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214392 - 31 Oct 2021

Cited by 5 | Viewed by 2044

Abstract

The extraction of building information with terrestrial laser scanning (TLS) has a number of important applications. As the density of projected points (DoPP) of facades is commonly greater than for other types of objects, building points can be extracted based on projection features. However, such methods usually suffer from density variation and parameter setting, as illustrated in previous studies. In this paper, we present a building extraction method for single-scan TLS data, mainly focusing on those problems. To adapt to the large density variation in TLS data, a filter using DoPP is applied on a polar grid, instead of a commonly used rectangular grid, to detect facade points. In DoPP filtering, the threshold to distinguish facades from other objects is generated adaptively for each cell by calculating the point number when placing the lowest building in it. Then, the DoPP filtering result is further refined by an object-oriented decision tree mainly based on grid features, such as compactness and horizontal hollow ratio. Finally, roof points are extracted by region growing on the non-facade points, using the highest point in each facade cell as a seed point. The experiments are conducted on two datasets with more than 1.7 billion points and with point density varying from millimeter to decimeter levels. The completeness and correctness of the first dataset containing more than 50 million points are 91.8% and 99.8%, with a running time of approximately 970 s. The second dataset is Semantic3D, of which the point number, completeness and correctness are about 1.65 billion, 90.2% and 94.5%, with a running time of about 14,464 s. The test shows that the proposed method achieves a better performance than previous grid-based methods and a similar level of accuracy to the point-based classification method and with much higher efficiency. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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19 pages, 17613 KiB

Open AccessArticle

Pole-Like Objects Segmentation and Multiscale Classification-Based Fusion from Mobile Point Clouds in Road Scenes

by Ziyang Wang, Lin Yang, Yehua Sheng and Mi Shen

Remote Sens. 2021, 13(21), 4382; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214382 - 30 Oct 2021

Cited by 7 | Viewed by 2076

Abstract

Real-time acquisition and intelligent classification of pole-like street-object point clouds are of great significance in the construction of smart cities. Efficient point cloud processing technology in road scenes can accelerate the development of intelligent transportation and promote the development of high-precision maps. However, available algorithms have the problems of incomplete extraction and the low recognition accuracy of pole-like objects. In this paper, we propose a segmentation method of pole-like objects under geometric structural constraints. As for classification, we fused the classification results at different scales with each other. First, the point cloud data excluding ground point clouds were divided into voxels, and the rod-shaped parts of the pole-like objects were extracted according to the vertical continuity. Second, the regional growth based on the voxel was carried out based on the rod part to retain the non-rod part of the pole-like objects. A one-way double coding strategy was adopted to preserve the details. For spatial overlapping entities, we used multi-rule supervoxels to divide them. Finally, the random forest model was used to classify the pole-like objects based on local- and global-scale features and to fuse the double classification results under the different scales in order to obtain the final result. Experiments showed that the proposed method can effectively extract the pole-like objects of the point clouds in the road scenes, indicating that the method can achieve high-precision classification and identification in the lightweight data. Our method can also bring processing inspiration for large data. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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31 pages, 81407 KiB

Open AccessArticle

A Multi-Analytical Study for the Enhancement and Accessibility of Archaeological Heritage: The Churches of San Nicola and San Basilio in Motta Sant’Agata (RC, Italy)

by Dario Giuffrida, Viviana Mollica Nardo, Daniela Neri, Giovanni Cucinotta, Irene Vittoria Calabrò, Loredana Pace and Rosina Celeste Ponterio

Remote Sens. 2021, 13(18), 3738; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183738 - 17 Sep 2021

Cited by 15 | Viewed by 3714

Abstract

In the coming years, Italy will need to take on a great challenge concerning the digitization of its archaeological and architectural heritage, one of the richest and most problematic in the world. The aim is to improve the knowledge, conservation, enhancement and accessibility of cultural assets and to make them a resource for national and local development. In this process, the next generation of 3D survey methods (laser scanning and photogrammetry), in combination with diagnostic techniques (spectroscopy analyses) and GIS/BIM (Geographic Information System/Building Information Modeling) solutions, represent a valid support. This work, part of a broader intervention launched by the Municipality of Reggio Calabria for the requalification of some archaeological sites located within its urban and metropolitan area, is focused on the study case of Motta S. Agata. The ancient settlement is located 8 km from Reggio C. in a hilly area difficult to reach and preserves numerous structures in a state of ruin. Among these, two interesting medieval churches are proposed for examination: the church of San Nicola, characterized by five hypogeal funeral crypts, and the chapel of San Basilio, which preserves the traces of a wall painting. A multi-methodological approach including close-range photogrammetry, laser scanning and chemical and thermal analyses was adopted in order to fulfill different tasks: creating a topographic model of the hillfort, mapping the archaeological evidence, digitizing and returning 3D models of the churches, characterizing materials through chemical analyses and monitoring the surfaces with thermal imaging. These combined applications have contributed to reaching the planned goals, i.e., study, conservation, diagnostics, preparation for restoration interventions, development of digital media and dissemination. In this way, a type of interactive museum (made up of virtual tours and informative digital models) has been made available in order to improve the site’s accessibility and inclusivity as well as to test the effect of digitization in attracting tourists and local people toward a place located outside of the usual tourist circuits. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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26 pages, 24919 KiB

Open AccessArticle

Quantitative Assessment of Changes in Topography of Town Caused by Human Impact, Krakow City Centre, Southern Poland

by Adam Łajczak, Roksana Zarychta and Grzegorz Wałek

Remote Sens. 2021, 13(12), 2286; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122286 - 10 Jun 2021

Cited by 4 | Viewed by 2103

Abstract

For the area of historic centre of Krakow (area 9.29 km²), southern Poland, base maps were prepared showing hypsometry and distribution of landforms in historical variant (ca. 1000 AD) and current variant, based on published data mainly from archaeological and geoengineering research carried out for the last 60 years, and including geographic information system (GIS) tools. The aim of the work is to establish changes in undulation of the area studied within the landforms (Vistula riverbed, Holocene alluvial plain, Pleistocene terrace, limestone hills) over the last millennium. Topographic parameters calculated on the basis of the base maps (local relative height, mean slope, limit of areas without aspect and with N, E, S and W aspects) were considered. These changes were linked with dominating trends of the altitude increase due to the development of large area embankments and of convex landforms. The assessment of changes of land undulation includes four authorial methods of quantitative determination of topography changes. Until the beginning of the 19th century land flattening occurred in most of the area of the town centre. Then the increase of local relative heights started to predominate which resulted in changes of other topographic parameters. Differentiated changes, both positive and negative, in the area undulation with altitude increase were determined. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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Review

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33 pages, 3014 KiB

Open AccessReview

High-Precision 3D Reconstruction for Small-to-Medium-Sized Objects Utilizing Line-Structured Light Scanning: A Review

by Bin Cui, Wei Tao and Hui Zhao

Remote Sens. 2021, 13(21), 4457; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13214457 - 06 Nov 2021

Cited by 20 | Viewed by 4528

Abstract

Three-dimensional reconstruction technology has demonstrated broad application potential in the industrial, construction, medical, forestry, agricultural, and pastural sectors in the last few years. High-quality digital point cloud information exists to help researchers to understand objects and environments. However, current research mainly focuses on making adaptive adjustments to various scenarios and related issues in the application of this technology rather than looking for further improvements and enhancements based on technical principles. Meanwhile, a review of approaches, algorithms, and techniques for high-precision 3D reconstruction utilizing line-structured light scanning, which is analyzed from a deeper perspective of elementary details, is lacking. This paper takes the technological path as the logical sequence to provide a detailed summary of the latest development status of each key technology, which will serve potential users and new researchers in this field. The focus is placed on exploring studies reconstructing small-to-medium-sized objects, as opposed to performing large-scale reconstructions in the field. Full article

(This article belongs to the Special Issue Laser Scanning and Point Cloud Processing in Urban Environments)

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