Special Issue "Aerial LiDAR Applications in Urban Environments"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Jesús Balado Frías
E-Mail Website
Guest Editor
Geotech Group, CINTECX, University of Vigo, 36310 Vigo, Spain
Interests: LiDAR; point cloud; laser scanner; computational geometry; machine learning; classification; deep learning; physical accessibility
Special Issues and Collections in MDPI journals
Dr. Lucía Díaz-Vilariño
E-Mail Website
Guest Editor
Applied Geotechnologies Research Group, Campus Universitario de Vigo, Universidade de Vigo, CINTECX, As Lagoas, Marcosende, 36310 Vigo, Spain
Interests: point cloud processing; 3D digital modeling; spatial analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

A great deal of information is concentrated in urban environments concentrate as most of the population lives in cities and surroundings. The population is increasing so rapidly that managing and interpreting urban data is often difficult. Urban expansion is happening unevenly, and even when planned, various unexpected problems can arise that endanger the comfort of citizens.

LiDAR systems have been demonstrated to be a way of acquiring urban environments quickly and accurately. Aerial laser scanning technology—installed in UAVs, aircraft, or helicopters—can capture large areas without being limited to the movement of the laser scanner on the ground. The availability of aerial point clouds is also becoming increasingly common, with entire cities and countries already having been captured.

The use of aerial LiDAR still faces some difficulties. On one hand, aerial point clouds are commonly affected by occlusions, causing an incomplete representation of the urban environment. On the other hand, aerial point clouds are composed of a massive number of coordinates that are required from the development of automated processing methods to extract the useful information from the application they are intended to be used.

This Special Issue will collect recent advances in the use of aerial LiDAR in urban environment applications. We welcome submissions that cover but are not limited to the following specific topics:

  • UAV-based LiDAR acquisition
  • Aerial LiDAR data quality: analysis and correction
  • Correction of occlusions in point cloud: methods and algorithms
  • 3D mathematical morphology applications
  • Advances in artificial intelligence for recognizing objects in aerial urban point clouds
  • Advances in urban reconstruction from aerial point clouds
  • Advances in LiDAR visualization: discrete and continuous level of detail
  • Direct applications of aerial point clouds
  • Applications of models obtained from aerial point clouds

Dr. Jesús Balado Frías
Dr. Lucía Díaz-Vilariño
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 papers will be 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 2400 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

  • Aerial point clouds
  • LiDAR
  • Urban environments
  • Level of detail
  • Smart cities
  • Data fusion

Published Papers (1 paper)

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Research

Article
Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping
Remote Sens. 2021, 13(4), 814; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040814 - 23 Feb 2021
Viewed by 678
Abstract
The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve [...] Read more.
The World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings to properly assess current plans and to develop new ones. This study investigates the effects of including conventional spectral signatures acquired by different sensors on the classification of airborne LiDAR (Light Detection and Ranging) point clouds using multiple feature spaces. The proposed method applied three machine learning algorithms—ML (Maximum Likelihood), SVM (Support Vector Machines), and MLP (Multilayer Perceptron Neural Network)—to classify LiDAR point clouds of a residential urban area after being geo-registered to aerial photos. The overall classification accuracy passed 97%, with height as the only geometric feature in the classifying space. Misclassifications occurred among different classes due to independent acquisition of aerial and LiDAR data as well as shadow and orthorectification problems from aerial images. Nevertheless, the outcomes are promising as they surpassed those achieved with large geometric feature spaces and are encouraging since the approach is computationally reasonable and integrates radiometric properties from affordable sensors. Full article
(This article belongs to the Special Issue Aerial LiDAR Applications in Urban Environments)
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