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Applications of Laser Scanning in Urban Environment
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Applications of Laser Scanning in Urban Environment

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 12652

Special Issue Editors

Dr. Henrique Lorenzo

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Guest Editor
Close-Range Remote Sensing & Photogrammetry Group, University of Vigo, EUET Forestal, Campus A Xunqueira s/n, 36005 Pontevedra, Spain
Interests: ground-penetrating radar; close-range photogrammetry; terrestrial laser scanner; cultural heritage applications
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Arias-Sánchez

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Guest Editor
Applied Geotechnologies Group, Department of Natural Resources and Environmental Engineering, School of Mining Engineering, University of Vigo, 36310 Vigo, Spain
Interests: smart cities; smart infrastructures; mobile mapping; laser scanning; close range photogrammetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of laser scanning has increased in the last two decades to obtain detailed representations of urban and natural environments. Regarding urban areas, a new generation of LiDAR systems mounted on a vehicle has been presented, called Mobile Mappers or LiDAR Cars, which acquire very dense data quickly and accurately, without disturbing the traffic.

In parallel to the new LiDAR devices, point-cloud-processing techniques have also been improved. Denser and more accurate point clouds have led to the detection of more types of features and more detailed modelling. In addition, the strong emergence of geo-intelligence is contributing to new applications of urban data.

This Special Issue is dedicated to publishing high-quality original research articles, reviews and applications on the use of Mobile Mappers in urban city environments, from a wide-ranging perspective. Potential topics include, but are not limited to, the following:

  • Data acquisition: road/street inventory, car/pedestrian safety, road/street maintenance, city mapping, building 3D mapping, transportation infrastructure mapping;
  • Point cloud and image processing: geo-intelligence, artificial intelligence, machine learning, deep learning, big data;
  • Digital city projects: modelling, digital twins, resource optimisation;
  • New sensors: multi-sensor, multi-spectral and multi-angular systems; autonomous driving, low-cost devices;
  • Reviews of the state of art in Mobile Laser Scanning.

Dr. Henrique Lorenzo
Dr. Pedro Arias-Sánchez
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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • LiDAR
  • Mobile Laser Scanning (MLS)
  • Mobile Mappers
  • Point cloud processing
  • Geo-intelligence

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Published Papers (6 papers)

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23 pages, 3947 KiB  
Article
Learnable Resized and Laplacian-Filtered U-Net: Better Road Marking Extraction and Classification on Sparse-Point-Cloud-Derived Imagery
by Miguel Luis Rivera Lagahit, Xin Liu, Haoyi Xiu, Taehoon Kim, Kyoung-Sook Kim and Masashi Matsuoka
Remote Sens. 2024, 16(23), 4592; https://doi.org/10.3390/rs16234592 - 6 Dec 2024
Viewed by 873
Abstract
High-definition (HD) maps for autonomous driving rely on data from mobile mapping systems (MMS), but the high cost of MMS sensors has led researchers to explore cheaper alternatives like low-cost LiDAR sensors. While cost effective, these sensors produce sparser point clouds, leading to [...] Read more.
High-definition (HD) maps for autonomous driving rely on data from mobile mapping systems (MMS), but the high cost of MMS sensors has led researchers to explore cheaper alternatives like low-cost LiDAR sensors. While cost effective, these sensors produce sparser point clouds, leading to poor feature representation and degraded performance in deep learning techniques, such as convolutional neural networks (CNN), for tasks like road marking extraction and classification, which are essential for HD map generation. Examining common image segmentation workflows and the structure of U-Net, a CNN, reveals a source of performance loss in the succession of resizing operations, which further diminishes the already poorly represented features. Addressing this, we propose improving U-Net’s ability to extract and classify road markings from sparse-point-cloud-derived images by introducing a learnable resizer (LR) at the input stage and learnable resizer blocks (LRBs) throughout the network, thereby mitigating feature and localization degradation from resizing operations in the deep learning framework. Additionally, we incorporate Laplacian filters (LFs) to better manage activations along feature boundaries. Our analysis demonstrates significant improvements, with F1-scores increasing from below 20% to above 75%, showing the effectiveness of our approach in improving road marking extraction and classification from sparse-point-cloud-derived imagery. Full article
(This article belongs to the Special Issue Applications of Laser Scanning in Urban Environment)
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18 pages, 4079 KiB  
Article
Patch-Based Surface Accuracy Control for Digital Elevation Models by Inverted Terrestrial Laser Scanning (TLS) Located on a Long Pole
by Juan F. Reinoso-Gordo, Francisco J. Ariza-López and José L. García-Balboa
Remote Sens. 2024, 16(23), 4516; https://doi.org/10.3390/rs16234516 - 2 Dec 2024
Cited by 1 | Viewed by 642
Abstract
Currently, many digital elevation models (DEMs) are derived from airborne LiDAR data acquisition flights. The vertical accuracy of both products has typically been evaluated using methods based on randomly sampled control points. However, due to the superficial nature of the DEM, logic suggests [...] Read more.
Currently, many digital elevation models (DEMs) are derived from airborne LiDAR data acquisition flights. The vertical accuracy of both products has typically been evaluated using methods based on randomly sampled control points. However, due to the superficial nature of the DEM, logic suggests that it is more appropriate to use a superficial object as an evaluation and control element, that is, a “control surface” or “control patch”. Our approach proposes a method for obtaining each patch from a georeferenced point cloud (PC) measured with a terrestrial laser scanner (TLS). In order to reduce the dilution of precision due to very acute angles of incidence that occur between the terrain and the scanner′s rays when it is stationed on a conventional tripod, a system has been created that allows the scanner to be placed face down at a height of up to 7 m. Stationing the scanner at that height also has the advantage of reducing shadow areas in the presence of possible obstacles. In our experiment, the final result is an 18 m × 18 m PC patch which, after resampling, can be transformed into a high-density (10,000 points/m2) and high-quality (absolute positional uncertainty < 0.05 m) DEM patch, that is, with a regular mesh format. This DEM patch can be used as the ground truth to assess the surface accuracy of DEMs (DEM format) or airborne LiDAR data acquisition flights (PC format). Full article
(This article belongs to the Special Issue Applications of Laser Scanning in Urban Environment)
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19 pages, 3132 KiB  
Article
Classification of Large-Scale Mobile Laser Scanning Data in Urban Area with LightGBM
by Eray Sevgen and Saygin Abdikan
Remote Sens. 2023, 15(15), 3787; https://doi.org/10.3390/rs15153787 - 30 Jul 2023
Cited by 12 | Viewed by 2426
Abstract
Automatic point cloud classification (PCC) is a challenging task in large-scale urban point clouds due to the heterogeneous density of points, the high number of points and the incomplete set of objects. Although recent PCC studies rely on automatic feature extraction through deep [...] Read more.
Automatic point cloud classification (PCC) is a challenging task in large-scale urban point clouds due to the heterogeneous density of points, the high number of points and the incomplete set of objects. Although recent PCC studies rely on automatic feature extraction through deep learning (DL), there is still a gap for traditional machine learning (ML) models with hand-crafted features, particularly after emerging gradient boosting machine (GBM) methods. In this study, we are using the traditional ML framework for the problem of PCC in large-scale datasets following the steps of neighborhood definition, multi-scale feature extraction, and classification. Different from others, our framework takes advantage of the fast feature calculation with multi-scale radius neighborhood and a recent state-of-the-art GBM classifier, LightGBM. We tested our framework using three mobile urban datasets, Paris–Rau–Madame, Paris–Rue–Cassette and Toronto3D. According to the results, our framework outperforms traditional machine learning models and competes with DL-based methods. Full article
(This article belongs to the Special Issue Applications of Laser Scanning in Urban Environment)
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17 pages, 9640 KiB  
Article
Laser Scanning for Terrain Analysis and Route Design for Electrified Public Transport in Urban Areas
by María Sánchez-Aparicio, Jose Antonio Martín-Jiménez, Enrique González-González and Susana Lagüela
Remote Sens. 2023, 15(13), 3325; https://doi.org/10.3390/rs15133325 - 29 Jun 2023
Cited by 1 | Viewed by 1558
Abstract
The orography of the terrain is a key factor for the electrification of vehicles, especially regarding public transport and electric buses. This work deals with the analysis of the use of mobile laser scanning, both terrestrial and aerial, for the evaluation of the [...] Read more.
The orography of the terrain is a key factor for the electrification of vehicles, especially regarding public transport and electric buses. This work deals with the analysis of the use of mobile laser scanning, both terrestrial and aerial, for the evaluation of the orography of urban areas. First, the minimum point density required is evaluated to estimate the slope. The results show that point densities of 1 point/m2, measured with aerial laser scanning, are adequate for the task. Based on this, the design of a route for public transport is presented including the requirements concerning key transit points, maximum slope, and others. Based on the proposed route design, the transformation to an electrified route is analyzed from an economic and environmental point of view. The results show that the implementation of electric buses vs. diesel buses in cities with steep slopes (up to 7%) reduces greenhouse gas emissions (32.59%) as well as economic costs (18.10%). Full article
(This article belongs to the Special Issue Applications of Laser Scanning in Urban Environment)
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20 pages, 3687 KiB  
Article
Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks
by Jesús Balado, Pedro Arias, Henrique Lorenzo and Adrián Meijide-Rodríguez
Remote Sens. 2021, 13(11), 2135; https://doi.org/10.3390/rs13112135 - 28 May 2021
Cited by 7 | Viewed by 2949
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applications of Laser Scanning in Urban Environment)
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25 pages, 11805 KiB  
Technical Note
Field Verification of Vehicle-Mounted All-Fiber Coherent Wind Measurement Lidar Based on Four-Beam Vertical Azimuth Display Scanning
by Xiaojie Zhang, Qingsong Li, Yujie Wang, Jing Fang and Yuefeng Zhao
Remote Sens. 2023, 15(13), 3377; https://doi.org/10.3390/rs15133377 - 1 Jul 2023
Cited by 3 | Viewed by 2117
Abstract
Wind parameters play a vital role in studying atmospheric dynamics and climate change. In this study, a vehicle-mounted coherent wind measurement Lidar (CWML) with a wavelength of 1.55 µm is demonstrated based on a four-beam vertical azimuth display (VAD) scanning mode, and a [...] Read more.
Wind parameters play a vital role in studying atmospheric dynamics and climate change. In this study, a vehicle-mounted coherent wind measurement Lidar (CWML) with a wavelength of 1.55 µm is demonstrated based on a four-beam vertical azimuth display (VAD) scanning mode, and a method to estimate wind vector from power spectrum is proposed. The feasibility of the application of wind profile Lidar in vehicles is verified by calibration tests, comparison experiments, and continuous observation experiments, successively. The effective detection height of Lidar can reach 3 km. In contrasting experiments, the correlation coefficients of the magnitude and direction of horizontal wind speed measured by vehicle-mounted Lidar and fixed Lidar are 0.94 and 0.91, respectively. The experimental results reveal that the accuracies of wind speed and direction measurements with the vehicle-mounted CWML are better than 0.58 m/s and 4.20°, respectively. Furthermore, to understand the role of the wind field in the process of energy and material transport further, a proton-transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) is utilized to measure the concentration of volatile organic compounds (VOCs). Relevant experimental results indicate that the local meteorological conditions, including wind speed and humidity, influence the VOC concentrations. Full article
(This article belongs to the Special Issue Applications of Laser Scanning in Urban Environment)
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