Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Print Special Issue Flyer
Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 18080

Share This 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

New Tools or Trends for Large-Scale Mapping and 3D Modelling in Remote Sensing (10 articles)

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

21 pages, 23062 KB

Open AccessArticle

Does Immersive VR Alter Landscape Perception? A Comparative Evaluation of UAV-Derived VR Versus 2D Imagery in Rural Villages

by Siya Zhao, Litao Zhu, Luyi Wang, Wenzheng Jia, Hao Wang, He Wu, Bo Wang and Wen Dai

Remote Sens. 2026, 18(16), 2818; https://doi.org/10.3390/rs18162818 - 20 Aug 2026

Viewed by 343

Abstract

Traditional rural landscape evaluations have generally relied on ground-level photographs or videos. However, these approaches have limitations in spatial continuity, depth cues, and interactivity. Unmanned Aerial Vehicle (UAV) photogrammetry and immersive virtual reality (VR) were integrated into a comparative rural landscape evaluation framework to assess landscape aesthetic quality. UAV-derived 3D village models were generated and deployed on PICO 4 headsets through Unity 3D and the Cesium plugin, providing evaluators with spatially continuous and 6DoF-enabled immersive representations of village scenes. The evaluation included ten landscape feature factors, including color harmony, vegetation richness, building layout harmony, openness of view, and sense of spatial depth. Ratings were collected from 75 valid participants across 17 villages, with village-level mean scores serving as the primary unit of inference. Paired-samples t-tests, subgroup sensitivity analysis, expert-only presentation-order sensitivity analysis, Pearson correlations, Steiger tests for dependent correlations, stepwise multiple linear regression, nested leave-one-village-out cross-validation (LOOCV), and bootstrap variable-selection stability analysis were conducted to examine differences between the 2D photo-based and VR-based conditions. The results showed that: (1) overall satisfaction was significantly higher in the VR-based condition than in the 2D photo-based condition (3.46 vs. 3.24); (2) the condition-specific regression models retained different landscape feature factors: sense of spatial depth and color harmony in the 2D photo-based model, and vegetation distribution pattern and environmental comfort in the VR-based model; and (3) the VR-based regression model had a higher condition-specific internal

R^{2}

than the 2D photo-based model (

R^{2} = 0.784

vs. 0.569). Within the present dataset, the VR-based model also showed lower SD-normalized prediction error under nested LOOCV, while bootstrap resampling showed higher selection frequencies for the predictors retained in the VR-based model. Overall, the findings demonstrate the potential of UAV-derived immersive VR for rural landscape evaluation and provide new evidence on how presentation conditions influence landscape perception and evaluation. 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

31 pages, 36482 KB

Open AccessArticle

Geo-Consistent Centralized Multi-UAV Gaussian SLAM for Incremental Orthophoto Generation

by Xiao Zhang, Shuaixin Li, Hongbin Dong, Xiaozhou Zhu, Haoxin Zhang and Baosong Deng

Remote Sens. 2026, 18(16), 2804; https://doi.org/10.3390/rs18162804 - 19 Aug 2026

Viewed by 411

Abstract

Online incremental orthophoto generation with multiple unmanned aerial vehicles (UAVs) remains challenging, as it requires accurate, efficient, and scalable mapping from distributed aerial observations. In this paper, we present a centralized GNSS-assisted multi-UAV 3D Gaussian Splatting SLAM framework for online incremental orthophoto mapping. Each UAV independently performs visual odometry to build local submaps, which are first aligned into a unified global coordinate system using GNSS constraints and further refined via inter-agent visual loop closures for improved cross-agent consistency. To enable scalable and high-quality mapping, we introduce two complementary Gaussian map maintenance modules: plane-guided grid-based collaborative densification, which improves mapping quality and accelerates convergence under multi-UAV conditions, and visibility-aware adaptive pruning, which effectively controls redundancy and memory usage. These components allow efficient joint optimization within a unified Gaussian representation. Experiments on multiple aerial datasets using video-derived image frames captured by consumer-grade UAV cameras demonstrate that the proposed system provides a favorable trade-off between geo-consistency, visual fidelity, and efficiency compared with existing methods. Quantitatively, the proposed method achieves a GCP RMSE of 2.32 m, completes multi-UAV orthophoto generation within 3.3–5.5 min, and reduces the total mapping time by approximately 35–55% compared with the corresponding single-UAV setting, while supporting online tracking and incremental orthophoto updates with bounded latency and memory consumption. 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

23 pages, 3303 KB

Open AccessArticle

LHEM-MSegNet: A Landslide Hazard Extraction Model Based on Multi-Source Data Fusion

by Fukang Shen, Weibin Li, Tianyi Zhang, Xuan Sun, Zhixiong Han and Xiaolei Yang

Remote Sens. 2026, 18(14), 2429; https://doi.org/10.3390/rs18142429 - 22 Jul 2026

Viewed by 501

Abstract

Accurate detection of loess landslide hazards is critical for disaster prevention, yet remains challenging due to complex terrain, spectral similarity to background regions, and the lack of specialized datasets. To address these challenges, this paper proposes LHEM-MSegNet, a multisource landslide hazard extraction model based on Transformer and attention fusion. A loess landslide hazard region segmentation dataset for Linyou County (LYPLH), Shaanxi Province, China, is constructed using a transfer-learning strategy based on the Segment Anything Model (SAM), followed by iterative manual correction and data augmentation to improve annotation quality. LHEM-MSegNet integrates Transformer blocks with spatial and channel attention modules to enhance feature representation, and adopts a multi-pathway architecture for multisource fusion. Specifically, optical remote sensing imagery is combined with geomorphological information, including DEM, slope, and aspect maps, to improve landslide hazard extraction. Experimental results on the LYPLH dataset demonstrate that the proposed LHEM-MSegNet achieves an mIoU of 89.29% by integrating optical imagery with geomorphological information. Ablation studies first demonstrate the effectiveness of the proposed model structure and further confirm the significant contribution of terrain-related features to landslide hazard extraction. In addition, the supplementary evaluation on the external Landslide4Sense benchmark shows that the proposed method maintains competitive performance under different data distributions. The proposed approach provides an effective solution for early loess landslide hazard identification and supports disaster mitigation in the Loess Plateau region. 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

27 pages, 17846 KB

Open AccessArticle

Multi-Model Machine Learning Mapping of Gully Erosion Susceptibility in the Heihe Region of the Xiaoxingán Mountains, China

by Jilin Zheng, Fanle Wan, Yanlong Cai, Junshuai Liu, Dake Wang, Xiaoyu Guo and Bowei Chen

Remote Sens. 2026, 18(11), 1844; https://doi.org/10.3390/rs18111844 - 4 Jun 2026

Cited by 2 | Viewed by 645

Abstract

Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95,

κ

= 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management. 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

20 pages, 8369 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2111

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; 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 KB

Open AccessArticle

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 20 | Viewed by 9510

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 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 KB

Open AccessArticle

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 13 | Viewed by 2837

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 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

Journal Menu

Journal Browser

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

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI