Special Issue "Remote Sensing with Geodetic Laser Scanning: Technologies and Methods for Data Acquisition"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Tania Landes
E-Mail Website
Guest Editor
Department of Surveying and Civil Engineering, National Institute of Applied Sciences of Strasbourg, 67084 Strasbourg, France
Interests: architectural laser scanning; mobile mapping systems; remote sensing; accuracy of data and 3D models
Prof. Dr. Pierre Grussenmeyer
E-Mail Website
Guest Editor
Department of Surveying and Civil Engineering, National Institute of Applied Sciences of Strasbourg, 67084 Strasbourg, France
Interests: close-range photogrammetry; architectural photogrammetry & laser scanning; mobile mapping systems and photogrammetric computer systems; integration and accuracy of data in 3D city and building models
Special Issues and Collections in MDPI journals
Dr. Grazia Tucci
E-Mail Website
Guest Editor
Dep. of Civil and Environmental Engineering (DICEA), University of Florence, 3 – 50139 Firenze, Italy
Interests: geomatics; laser scanner; photogrammetry; GIS/BIM; landscape; Built Heritage
Special Issues and Collections in MDPI journals
Prof. Dr. Stephan Nebiker
E-Mail Website
Guest Editor
Institute of Geomatics Engineering, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, 4132 Muttenz, Switzerland
Interests: photogrammetry; 3D imaging; mobile mapping; unmanned aerial vehicles; remote sensing; cloud computing; infrastructure management; smart cities

Special Issue Information

Dear Colleagues,

Progress in geodetic laser scanning technologies has led to sensors and methods with growing capabilities in automated acquisition and processing. These topics deserve to be deepened in a Special Issue entitled “Remote Sensing with Geodetic Laser Scanning: Technologies and Methods for Data Acquisition”.

Recently, geodetic scanning systems have been developed in the way to perform registration in real time during the scanning of more or less complex areas. New trends in real-time registration systems and georeferencing solutions will be of concern for this Special Issue, as will innovative solutions based on localization and positioning using LiDAR SLAM.

An important aspect is also the question of assessing the accuracy of recent laser scanning systems. Accuracy assessment of acquired data in relation to the network design, as well as the accuracy of produced models and accurate multisensor calibration methods, remains a big challenge in the era of digital transition.

The fusion of laser scanners and multispectral sensors in static or mobile mapping systems opens a wide area of applications. Their efficiency for vegetation discrimination, cultural heritage documentation, climatological challenges, and real-time monitoring will be emphasized in this Special Issue.

Moreover, point cloud processing is evolving to be of even greater assistance to users in the production of final models. The trend of using deep learning methods for improving semantic segmentation impacts the whole “scan-to-BIM” workflow. The advantages of simultaneous object detection and recognition in the acquisition process will be highlighted.

Dr. Tania Landes
Prof. Dr. Pierre Grussenmeyer
Prof. Dr. Grazia Tucci
Prof. Dr. Stephan Nebiker
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

  • real-time registration
  • LiDAR SLAM
  • multi-sensor fusion
  • multi-sensor calibration
  • object detection
  • semantic segmentation
  • deep learning
  • accuracy assessment

Published Papers (1 paper)

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Research

Article
Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method
Remote Sens. 2021, 13(5), 859; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050859 - 25 Feb 2021
Viewed by 662
Abstract
Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for [...] Read more.
Classification of aerial point clouds with high accuracy is significant for many geographical applications, but not trivial as the data are massive and unstructured. In recent years, deep learning for 3D point cloud classification has been actively developed and applied, but notably for indoor scenes. In this study, we implement the point-wise deep learning method Dynamic Graph Convolutional Neural Network (DGCNN) and extend its classification application from indoor scenes to airborne point clouds. This study proposes an approach to provide cheap training samples for point-wise deep learning using an existing 2D base map. Furthermore, essential features and spatial contexts to effectively classify airborne point clouds colored by an orthophoto are also investigated, in particularly to deal with class imbalance and relief displacement in urban areas. Two airborne point cloud datasets of different areas are used: Area-1 (city of Surabaya—Indonesia) and Area-2 (cities of Utrecht and Delft—the Netherlands). Area-1 is used to investigate different input feature combinations and loss functions. The point-wise classification for four classes achieves a remarkable result with 91.8% overall accuracy when using the full combination of spectral color and LiDAR features. For Area-2, different block size settings (30, 50, and 70 m) are investigated. It is found that using an appropriate block size of, in this case, 50 m helps to improve the classification until 93% overall accuracy but does not necessarily ensure better classification results for each class. Based on the experiments on both areas, we conclude that using DGCNN with proper settings is able to provide results close to production. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: QUALITY ASSESSMENT OF SLAM SYSTEM IN FORESTAL ENVIROMENTAL FOR ARCHAEOLOGICAL SURVEY
Authors: Marco Limongiello 1, Alessandro Di Benedetto 1*, Diego Ronchi 2
Affiliation: 1 Department of Civil Engineering, University of Salerno, 132, 84084 Fisciano (Salerno), Italy 2 Spiron Heritage and Survey, 00198 Rome, Italy
Abstract: In the field of archaeological survey, remote sensors and laser scanner systems are widely used to create 3D models. The choice of the method to be used depends mainly on the complexity of the site under investigation, the accuracy requirements and the available budget and time. However, careful analysis of the point cloud is also required to obtain reliable and high-quality products. The project aimed to systematically investigate the archaeological remains of the imperial Domitian villa in Sabaudia (Italy), using different three-dimensional survey techniques. Particular attention in the research was paid to the identification and documentation of traces that buried structures left on the surface occupied by the villa. The accuracy of the 3D model is a function of the scale of the representation useful to represent small geomorphological and structural details with their typical shape, allowing them to be immediately perceived. In this work we want to evaluate the accuracy of the point clouds produced by a last generation Leica BLK2GO mobile system, able to acquire information quickly and efficiently. The complexity of the case study (vegetation, tall trees, emerged and buried structures) makes the scene ideal for validation purposes. The derived products will be compared with those obtained from terrestrial laser scanner (TLS) measurements, a detailed analysis will also be carried out using a plano/altitude framing network and local measurements acquired by total station. The requirement of the project is the 3D reconstruction of the whole area at an accuracy that stands between a big-scale environmental survey and a small-scale architectural survey.

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