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Advancements in LiDAR Technology and Applications in Remote Sensing

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

Deadline for manuscript submissions: 28 May 2025 | Viewed by 793

Special Issue Editor


E-Mail Website
Guest Editor
1. Engineering Physics Group, School of Aerospace Engineering, University of Vigo, Campus Ourense, 32004 Ourense, Spain
2. Telespazio Ibérica, Avenida de Manoteras nº18, Planta 5 Puerta 1 Oficina 3, 28050 Madrid, Spain
Interests: LiDAR; remote sensing; image processing; UAV

Special Issue Information

Dear Colleagues,

LiDAR technology has become one of the most important tools for different types of inspections and monitoring tasks. In recent years, LiDAR sensors have become less expensive, their accuracy and scanning speed have increased, and their weight has decreased. All this has enabled this technology to be used in different fields, such the inspection of infrastructures, roads, and power lines. This technology can be used for biodiversity monitoring tasks, such as forest monitoring and inventory, among others. In addition, the combination of this technology with autonomous vehicles such as UAVs has allowed for the use of these sensors in new applications, such as the inspection of hard-to-access infrastructures, such as bridges and wind turbines.

This Special Issue aims at showcasing studies on disruptive applications of LiDAR technology in various fields, showing the new ways in which it can be applied, or the optimization of its current applications. This Special Issue is focused on LiDAR technology and point cloud processing algorithms, but studies in which LiDAR is used in combination with other sensors, such as different kinds of cameras or radar sensors, are also welcome. Articles may address, but are not limited, to the following topics:

  • Infrastructure inspections;
  • Biodiversity monitoring;
  • Power line inspections;
  • Wind turbine inspections;
  • Machine learning algorithms for point cloud processing;
  • Scan-to-BIM algorithms.

Dr. Luis Miguel González de Santos
Guest Editor

Name: Enrique Aldao Pensado
Guest Editor Assistant
Email: [email protected]
Address: Research Institute of Physics and Aerospace Sciences (IFCAE), University of Vigo, Campus of As Lagoas, 32004 Ourense, Spain
Webpage: https://portalcientifico.uvigo.gal/investigadores/875165/detalle
Interests: LiDAR; remote sensing; machine learning; IA based algorithms

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
  • point cloud processing
  • UAV
  • infrastructure monitoring
  • machine learning
  • power lines inspection

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Published Papers (1 paper)

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Research

27 pages, 6755 KiB  
Article
Fusing LiDAR and Photogrammetry for Accurate 3D Data: A Hybrid Approach
by Rytis Maskeliūnas, Sarmad Maqsood, Mantas Vaškevičius and Julius Gelšvartas
Remote Sens. 2025, 17(3), 443; https://doi.org/10.3390/rs17030443 - 28 Jan 2025
Viewed by 325
Abstract
The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as [...] Read more.
The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications. Full article
(This article belongs to the Special Issue Advancements in LiDAR Technology and Applications in Remote Sensing)
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