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Remote Sensing
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Lidar Sensing for 3D Digital Twins
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Lidar Sensing for 3D Digital Twins

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

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 4501

Special Issue Editors

Prof. Dr. Wen Xiao

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: lidar mapping; 3D vision; change detection
Special Issues, Collections and Topics in MDPI journals
Dr. Yongqiang Li

E-Mail Website
Guest Editor
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Interests: lidar mapping; 3D modeling; change detection
Dr. Jian Yang

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: fluorescence/multispectral lidar; UAV lidar; classification based on multispectral/hyperspectral point clouds; power-line inspection; vegetation parameter retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Lidar has now become a common sensor for direct and accurate 3D information acquisition. It has been used for both environment perception in computer vision and robotics communities and 3D mapping in remote sensing and spatial surveying communities, depending on its configurations (number of beams, rotating mechanisms, spectral characters) and mounting platforms. Digital twins is now a well-adopted concept to guide technological development in data acquisition, physical modelling, information management, interactive visualisation, etc. A digital twin generally includes the static environment and the dynamic components that interact with the environment. Thanks to the versatility of lidar systems, they have been used for both static environment mapping and dynamics detection.

This Special Issue invites contributions from both remote sensing and related communities to demonstrate the advancements in lidar-based 3D mapping, dynamic object detection and tracking towards digital twins.

Potential topics include, but are not limited to the application of lidar in areas of:

  • Urban-scale mapping and modelling;
  • Point cloud segmentation;
  • Object detection and tracking;
  • Change/dynamic detection;
  • Multi-spectral lidar data processing;
  • Lidar-based mapping systems.

Prof. Dr. Wen Xiao
Dr. Yongqiang Li
Dr. Jian Yang
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.

Published Papers (3 papers)

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20 pages, 18836 KiB  
Article
Analysis of Light Obstruction from Street Lighting in Road Scenes
by Jingzhi Ren, Yongqiang Li, Huiyun Liu, Kanghong Li, Daoqian Hao and Zhiyao Wang
Remote Sens. 2023, 15(24), 5655; https://doi.org/10.3390/rs15245655 - 7 Dec 2023
Viewed by 1345
Abstract
As urban greenery improves and the ecological environment is continuously optimized, road facilities are also impacted to varying degrees. For example, as vegetation grows, it causes varying degrees of obstruction to the lighting facilities on the roads. This article is based on vehicle-mounted [...] Read more.
As urban greenery improves and the ecological environment is continuously optimized, road facilities are also impacted to varying degrees. For example, as vegetation grows, it causes varying degrees of obstruction to the lighting facilities on the roads. This article is based on vehicle-mounted LiDAR data and focuses on the point cloud data characteristics of different objects. Using appropriate modeling techniques, it accurately models road surfaces, green belts, streetlights, and other objects. On the Lumion platform, this system creates a 3D visualization of road scenes and examines the interplay between objects and lighting space, analyzing lit areas. Leveraging the precise 3D spatial relationships found in point clouds, it determines the effective illumination area on the ground from streetlights after object obstruction, comparing it to the theoretical illumination area. This not only visualizes the road scene but also quantifies the lighting obstruction rate. Furthermore, it assesses the lighting conditions in road scenes based on illuminance distribution, offering scientific insights and suggestions for enhancing road lighting. Full article
(This article belongs to the Special Issue Lidar Sensing for 3D Digital Twins)
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21 pages, 20260 KiB  
Article
Assessment of Leica CityMapper-2 LiDAR Data within Milan’s Digital Twin Project
by Marica Franzini, Vittorio Marco Casella and Bruno Monti
Remote Sens. 2023, 15(21), 5263; https://doi.org/10.3390/rs15215263 - 6 Nov 2023
Viewed by 1028
Abstract
The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. [...] Read more.
The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM. Full article
(This article belongs to the Special Issue Lidar Sensing for 3D Digital Twins)
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Other

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14 pages, 5316 KiB  
Technical Note
Removing Moving Objects without Registration from 3D LiDAR Data Using Range Flow Coupled with IMU Measurements
by Yi Cai, Bijun Li, Jian Zhou, Hongjuan Zhang and Yongxing Cao
Remote Sens. 2023, 15(13), 3390; https://doi.org/10.3390/rs15133390 - 3 Jul 2023
Viewed by 1154
Abstract
Removing moving objects from 3D LiDAR data plays a crucial role in advancing real-time odometry, life-long SLAM, and motion planning for robust autonomous navigation. In this paper, we present a novel method aimed at addressing the challenges faced by existing approaches when dealing [...] Read more.
Removing moving objects from 3D LiDAR data plays a crucial role in advancing real-time odometry, life-long SLAM, and motion planning for robust autonomous navigation. In this paper, we present a novel method aimed at addressing the challenges faced by existing approaches when dealing with scenarios involving significant registration errors. The proposed approach offers a unique solution for removing moving objects without the need for registration, leveraging range flow estimation combined with IMU measurements. To this end, our method performs global range flow estimation by utilizing geometric constraints based on the spatio-temporal gradient information derived from the range image, and we introduce IMU measurements to further enhance the accuracy of range flow estimation. Through extensive quantitative evaluations, our approach showcases an improved performance, with an average mIoU of 45.8%, surpassing baseline methods such as Removert (43.2%) and Peopleremover (32.2%). Specifically, it exhibits a substantial improvement in scenarios characterized by a deterioration in registration performance. Moreover, our method does not rely on costly annotations, which make it suitable for SLAM systems with different sensor setups. Full article
(This article belongs to the Special Issue Lidar Sensing for 3D Digital Twins)
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