Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
New Tools or Trends for Large-Scale Mapping and 3D Modelling...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

New Tools or Trends for Large-Scale Mapping and 3D Modelling (Second Edition)

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

Deadline for manuscript submissions: 15 August 2025 | Viewed by 5046

Special Issue Editors

Prof. Dr. Tarig Ali

E-Mail Website
Guest Editor
Department of Civil Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
Interests: GIS and mapping; applied remote sensing; spatial analysis; large-scale mapping; 3D GIS; LiDAR mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jorge Delgado García

E-Mail Website
Guest Editor
Department of Cartographic, Geodetic and Photogrammetric Engineering, University of Jaén, 23071 Jaén, Spain
Interests: geomatics; photogrammetry; remote sensing; geostatistics; LiDAR; RPAS; 3D modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Fayez Tarsha Kurdi

E-Mail Website
Guest Editor
Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia
Interests: remote sensing; Lidar; 3D modelling; classification; segmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Topographic surveys are used to capture the shape of the earth’s surface, which provide the information needed for 2D or 3D representations. The general trend focuses nowadays on 3D models that facilitate management and semantic information extraction. Large-scale topographic maps are essential for (a) the design and construction of the infrastructure in the urban and rural areas, (b) vegetation analysis and monitoring, (c) 3D and city modelling, and (d) general-purpose mapping. Topographic surveys are normally carried out with traditional surveying, photogrammetry, LiDAR/laser scanning, and satellite remote sensing. Mobile mapping using terrestrial vehicles or airborne aircraft accelerates data acquisition process. The integration of Unmanned Aerial Vehicles (UAVs) in measurement operations not only increases the efficacity of data collection and improves the resolution, but also adds a new aspect concerning cost, speed, availability, and safety.

Very-high-resolution 3D information can be used to create  Digital Twins , which have become popular and useful tool for creating virtual representations of physical objects and systems. Indeed, they improve the performance of thematic systems such as City Information Modelling (CIM), Building Information Modelling (BIM), Land Information Modelling (LAM), and Tree Information Modelling (TIM). These systems sustain real-time monitoring and management of spatial items to realize sustainable development in a fast-varying world.

Remote sensing tools have shown their efficacy in exploring the natural, human, and social systems at unprecedented resolutions. These tools have been used for acquiring the spatial data needed for mapping since the early 1970s because they are rapid, cost-effective, and reliable.

Meanwhile, the use of machine learning techniques for data classification as well as data modelling plays a major role simultaneously with rule-based approaches for increasing the automatization of data processing.

Now, the demand for geospatial data has increased exponentially, coupled with the need for high-quality large-scale maps and 3D models. The recent developments in remote sensing sensors have opened the door for the high-quality, large-scale mapping of our environment, 3D/city modelling, as well as many useful applications such as infrastructure monitoring and crack measurement.

This is the second volume of the Special Issue of Remote Sensing on "New Tools or Trends for Large-Scale Mapping and 3D Modelling". In this Special Issue, we aim to compile research articles that address various aspects of large-scale mapping and 3D modelling with remote sensing sensors from field data acquisition used to map or 3D-model, and their applications. Review contributions and papers describing new sensors/concepts are also welcomed.

Prof. Dr. Tarig Ali
Prof. Dr. Jorge Delgado García
Dr. Fayez Tarsha Kurdi
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

  • remote sensing
  • topographic mapping
  • mobile mapping
  • data acquisition
  • sensors
  • LiDAR
  • UAVs (drone)
  • feature extraction
  • 3D modelling
  • machine learning
  • data classification
  • forests modelling
  • digital twins
  • city information modelling (CIM)
  • building information modelling (BIM)
  • land information modelling (LAM)
  • tree information modelling (TIM)
  • processing quality

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 8369 KiB  
Article
A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting
by Maria Krivova, Michael J. Olsen and Ben A. Leshchinsky
Remote Sens. 2025, 17(5), 815; https://doi.org/10.3390/rs17050815 - 26 Feb 2025
Viewed by 467
Abstract
Erosion poses a significant threat to infrastructure and ecosystems on coastlines worldwide. Public infrastructure such as US 101—a critical conduit linking coastal communities and renowned destinations—can be costly to maintain due to erosion hazards. Erosion is episodic and varies both spatially and temporarily; [...] Read more.
Erosion poses a significant threat to infrastructure and ecosystems on coastlines worldwide. Public infrastructure such as US 101—a critical conduit linking coastal communities and renowned destinations—can be costly to maintain due to erosion hazards. Erosion is episodic and varies both spatially and temporarily; hence, forecasting erosion patterns to identify vulnerable infrastructure is immensely challenging. This study presents an innovative Geographic Information Systems (GIS) algorithm to forecast sea cliff erosion progression utilizing imagery datasets (hereafter referred to as ‘rasters’). This approach is demonstrated for an approximately 300 m segment of sea cliffs near Spencer Creek Bridge in Beverly Beach State Park, Oregon, USA. First, Digital Elevation Model (DEM) rasters are created from multiple epochs of terrestrial lidar point clouds using two approaches: Triangular Irregular Networks (TINs) and Empirical Bayesian Kriging (EBK). These DEMs were integrated into a multidimensional raster to generate trend rasters. Based on these trend rasters, forecast DEMs were created based on several different combinations of training and forecast epochs. The forecast DEMs were evaluated against the original lidar data, to calculate residuals to determine optimal model parameters. It was revealed that four combinations warrant particular attention: EBK with harmonic and linear regression of trend rasters, and TIN with harmonic and linear regression of trend rasters. These methods demonstrate consistent decreases in residuals as the number of epochs used for interpolation increases. Under these circumstances, it is expected that the forecasting DEMs will exhibit residuals lower than 10 cm. This outcome is contingent on the condition that the time between the epochs used for prediction and the forecasted epochs does not increase. Full article
(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling (Second Edition))
►▼ Show Figures

Graphical abstract

19 pages, 3353 KiB  
Article
Assessment of NavVis VLX and BLK2GO SLAM Scanner Accuracy for Outdoor and Indoor Surveying Tasks
by Zahra Gharineiat, Fayez Tarsha Kurdi, Krish Henny, Hamish Gray, Aaron Jamieson and Nicholas Reeves
Remote Sens. 2024, 16(17), 3256; https://doi.org/10.3390/rs16173256 - 2 Sep 2024
Cited by 2 | Viewed by 2445
Abstract
The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper [...] Read more.
The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. Full article
(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling (Second Edition))
►▼ Show Figures

Figure 1

19 pages, 8806 KiB  
Article
Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data
by Fayez Tarsha Kurdi, Elżbieta Lewandowicz, Zahra Gharineiat and Jie Shan
Remote Sens. 2024, 16(12), 2220; https://doi.org/10.3390/rs16122220 - 19 Jun 2024
Cited by 5 | Viewed by 1343
Abstract
This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection and Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the [...] Read more.
This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection and Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, each of which is further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. For a tree dimensions estimation, the study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees. The maximum absolute quantitative accuracy equals 0.49 m, and the maximum relative absolute error equals 0.29%. Full article
(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling (Second Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop