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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 24777

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Special Issue Editors

Prof. Dr. Tarig Ali

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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
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Prof. Dr. Jorge Delgado García

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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
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Dr. Fayez Tarsha Kurdi

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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 world and represent it as a topographic map or a three-dimensional (3D) model. Large-scale topographic maps are essential for (a) the design and construction of the infrastructure in the urban environment, (b) 3D/city modelling, and (c) general-purpose mapping. Topographic surveys are normally carried out with traditional surveying, photogrammetry, LiDAR/laser scanning, unmanned aerial vehicles (UAVs), and satellite remote sensing. 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. 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 cameras 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, crack measurement, etc. This includes depth (stereo) cameras, fine-resolution satellite sensors, and state-of-the-art cameras for terrestrial photogrammetric applications.

In this Special Issue, we aim to compile research articles that address various aspects of large-scale mapping and 3D/city modelling with remote sensing cameras 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
Prof. 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

topographic mapping
cameras
3D and city modelling
UAVs
mapping/3D modelling

Related Special Issue

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

Published Papers (10 papers)

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Research

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17 pages, 8889 KiB

Open AccessArticle

Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data

by Fayez Tarsha Kurdi, Elżbieta Lewandowicz, Zahra Gharineiat and Jie Shan

Remote Sens. 2023, 15(13), 3324; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15133324 - 29 Jun 2023

Cited by 2 | Viewed by 967

Abstract

The development of autonomous navigation systems requires digital building models at the LoD3 level. Buildings with atypically shaped features, such as turrets, domes, and chimneys, should be selected as landmark objects in these systems. The aim of this study was to develop a method that automatically transforms segmented LiDAR (Light Detection And Ranging) point cloud to create such landmark building models. A detailed solution was developed for selected buildings that are solids of revolution. The algorithm relies on new methods for determining building axes and cross-sections. To handle the gaps in vertical cross-sections due to the absence of continuous measurement data, a new strategy for filling these gaps was proposed based on their automatic interpretation. In addition, potential points associated with building ornaments were used to improve the model. The results were presented in different stages of the modeling process in graphic models and in a matrix recording. Our work demonstrates that complicated buildings can be represented with a light and regular data structure. Further investigations are needed to estimate the constructed building model with vectorial models. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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24 pages, 26060 KiB

Open AccessArticle

Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes

by Chuanyu Fu, Nan Huang, Zijie Huang, Yongjian Liao, Xiaoming Xiong, Xuexi Zhang and Shuting Cai

Remote Sens. 2023, 15(9), 2474; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092474 - 08 May 2023

Viewed by 1210

Abstract

Multiview stereo (MVS) achieves efficient 3D reconstruction on Lambertian surfaces and strongly textured regions. However, the reconstruction of weakly textured regions, especially planar surfaces in weakly textured regions, still faces significant challenges due to the fuzzy matching problem of photometric consistency. In this paper, we propose a multiview stereo for recovering planar surfaces guided by confidence calculations, resulting in the construction of large-scale 3D models for high-resolution image scenes. Specifically, a confidence calculation method is proposed to express the reliability degree of plane hypothesis. It consists of multiview consistency and patch consistency, which characterize global contextual information and local spatial variation, respectively. Based on the confidence of plane hypothesis, the proposed plane supplementation generates new reliable plane hypotheses. The new planes are embedded in the confidence-driven depth estimation. In addition, an adaptive depth fusion approach is proposed to allow regions with insufficient visibility to be effectively fused into the dense point clouds. The experimental results illustrate that the proposed method can lead to a 3D model with competitive completeness and high accuracy compared with state-of-the-art methods. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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19 pages, 9128 KiB

Open AccessArticle

A Quantitative Assessment of LIDAR Data Accuracy

by Ahmed Elaksher, Tarig Ali and Abdullatif Alharthy

Remote Sens. 2023, 15(2), 442; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020442 - 11 Jan 2023

Cited by 6 | Viewed by 2893

Abstract

Airborne laser scanning sensors are impressive in their ability to collect a large number of topographic points in three dimensions in a very short time thus providing a high-resolution depiction of complex objects in the scanned areas. The quality of any final product naturally depends on the original data and the methods of generating it. Thus, the quality of the data should be evaluated before assessing any of its products. In this research, a detailed evaluation of a LIDAR system is presented, and the quality of the LIDAR data is quantified. This area has been under-emphasized in much of the published work on the applications of airborne laser scanning data. The evaluation is done by field surveying. The results address both the planimetric and the height accuracy of the LIDAR data. The average discrepancy of the LIDAR elevations from the surveyed study area is 0.12 m. In general, the RMSE of the horizontal offsets is approximately 0.50 m. Both relative and absolute height discrepancies of the LIDAR data have two components of variation. The first component is a random short-period variation while the second component has a less significant frequency and depends on the biases in the geo-positioning system. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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19 pages, 11179 KiB

Open AccessArticle

New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans

by Jonathan L. Batchelor, Todd M. Wilson, Michael J. Olsen and William J. Ripple

Remote Sens. 2023, 15(1), 145; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010145 - 27 Dec 2022

Cited by 4 | Viewed by 2241

Abstract

We developed new measures of structural complexity using single point terrestrial laser scanning (TLS) point clouds. These metrics are depth, openness, and isovist. Depth is a three-dimensional, radial measure of the visible distance in all directions from plot center. Openness is the percent of scan pulses in the near-omnidirectional view without a return. Isovists are a measurement of the area visible from the scan location, a quantified measurement of the viewshed within the forest canopy. 243 scans were acquired in 27 forested stands in the Pacific Northwest region of the United States, in different ecoregions representing a broad gradient in structural complexity. All stands were designated natural areas with little to no human perturbations. We created “structural signatures” from depth and openness metrics that can be used to qualitatively visualize differences in forest structures and quantitively distinguish the structural composition of a forest at differing height strata. In most cases, the structural signatures of stands were effective at providing statistically significant metrics differentiating forests from various ecoregions and growth patterns. Isovists were less effective at differentiating between forested stands across multiple ecoregions, but they still quantify the ecological important metric of occlusion. These new metrics appear to capture the structural complexity of forests with a high level of precision and low observer bias and have great potential for quantifying structural change to forest ecosystems, quantifying effects of forest management activities, and describing habitat for organisms. Our measures of structure can be used to ground truth data obtained from aerial lidar to develop models estimating forest structure. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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16 pages, 2909 KiB

Open AccessArticle

3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data

by Elżbieta Lewandowicz, Fayez Tarsha Kurdi and Zahra Gharineiat

Remote Sens. 2022, 14(19), 4687; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194687 - 20 Sep 2022

Cited by 7 | Viewed by 1901

Abstract

This paper presents an innovative approach to the automatic modeling of buildings composed of rotational surfaces, based exclusively on airborne LiDAR point clouds. The proposed approach starts by detecting the gravity center of the building’s footprint. A thin point slice parallel to one coordinate axis around the gravity center was considered, and a vertical cross-section was rotated around a vertical axis passing through the gravity center, to generate the 3D building model. The constructed model was visualized with a matrix composed of three matrices, where the same dimensions represented the X, Y, and Z Euclidean coordinates. Five tower point clouds were used to evaluate the performance of the proposed algorithm. Then, to estimate the accuracy, the point cloud was superimposed onto the constructed model, and the deviation of points describing the building model was calculated, in addition to the standard deviation. The obtained standard deviation values, which express the accuracy, were determined in the range of 0.21 m to 1.41 m. These values indicate that the accuracy of the suggested method is consistent with approaches suggested previously in the literature. In the future, the obtained model could be enhanced with the use of points that have considerable deviations. The applied matrix not only facilitates the modeling of buildings with various levels of architectural complexity, but it also allows for local enhancement of the constructed models. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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15 pages, 5957 KiB

Open AccessArticle

Strategies for the Storage of Large LiDAR Datasets—A Performance Comparison

by Juan A. Béjar-Martos, Antonio J. Rueda-Ruiz, Carlos J. Ogayar-Anguita, Rafael J. Segura-Sánchez and Alfonso López-Ruiz

Remote Sens. 2022, 14(11), 2623; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112623 - 31 May 2022

Cited by 1 | Viewed by 3012

Abstract

The widespread use of LiDAR technologies has led to an ever-increasing volume of captured data that pose a continuous challenge for its storage and organization, so that it can be efficiently processed and analyzed. Although the use of system files in formats such as LAS/LAZ is the most common solution for LiDAR data storage, databases are gaining in popularity due to their evident advantages: centralized and uniform access to a collection of datasets; better support for concurrent retrieval; distributed storage in database engines that allows sharding; and support for metadata or spatial queries by adequately indexing or organizing the data. The present work evaluates the performance of four popular NoSQL and relational database management systems with large LiDAR datasets: Cassandra, MongoDB, MySQL and PostgreSQL. To perform a realistic assessment, we integrate these database engines in a repository implementation with an elaborate data model that enables metadata and spatial queries and progressive/partial data retrieval. Our experimentation concludes that, as expected, NoSQL databases show a modest but significant performance difference in favor of NoSQL databases, and that Cassandra provides the best overall database solution for LiDAR data. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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30 pages, 1295 KiB

Open AccessArticle

Comparative Approach of Unmanned Aerial Vehicle Restrictions in Controlled Airspaces

by Stephen John McTegg, Fayez Tarsha Kurdi, Shane Simmons and Zahra Gharineiat

Remote Sens. 2022, 14(4), 822; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040822 - 09 Feb 2022

Cited by 11 | Viewed by 2798

Abstract

Recent public discourse regarding unmanned aerial vehicle (UAV) usage and regulation is focused around public privacy and safety. Most authorities have employed key guidelines and licensing procedures for piloting UAVs, however there is marginal consensus amongst regulators and a limited view towards unified procedures. This paper aims to analyze the key challenges that affect the use of UAVs and to determine if the current rules address those challenges. For this purpose: privacy, safety, security, public nuisance and trespass are tested. A set of criteria are developed to perform a comparative analysis against the existing UAV regulations to determine how they are meeting the specified criteria. Within this framework, five countries are selected: Australia, Canada, European Union (EU), United Kingdom (UK) and the United States of America (USA), with usage data and length of time between regulatory reviews ensuring any analysis is realized on updated protocols. The regulations of each country are then compared against the developed criteria. The findings show there are shortfalls with the majority of regulations failing to meet some criteria and the results confirm that key issues fail to be addressed. Finally, recommendations are suggested for filling the gaps in the regulations. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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23 pages, 8791 KiB

Open AccessArticle

Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof

by Fayez Tarsha Kurdi, Zahra Gharineiat, Glenn Campbell, Mohammad Awrangjeb and Emon Kumar Dey

Remote Sens. 2022, 14(2), 430; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020430 - 17 Jan 2022

Cited by 13 | Viewed by 2871

Abstract

This paper suggests a new algorithm for automatic building point cloud filtering based on the Z coordinate histogram. This operation aims to select the roof class points from the building point cloud, and the suggested algorithm considers the general case where high trees are associated with the building roof. The Z coordinate histogram is analyzed in order to divide the building point cloud into three zones: the surrounding terrain and low vegetation, the facades, and the tree crowns and/or the roof points. This operation allows the elimination of the first two classes which represent an obstacle toward distinguishing between the roof and the tree points. The analysis of the normal vectors, in addition to the change of curvature factor of the roof class leads to recognizing the high tree crown points. The suggested approach was tested on five datasets with different point densities and urban typology. Regarding the results’ accuracy quantification, the average values of the correctness, the completeness, and the quality indices are used. Their values are, respectively, equal to 97.9%, 97.6%, and 95.6%. These results confirm the high efficacy of the suggested approach. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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Review

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61 pages, 16989 KiB

Open AccessReview

Spatial Validation of Spectral Unmixing Results: A Systematic Review

by Rosa Maria Cavalli

Remote Sens. 2023, 15(11), 2822; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112822 - 29 May 2023

Cited by 3 | Viewed by 2027

Abstract

The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011 and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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24 pages, 3114 KiB

Open AccessReview

Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques

by Zahra Gharineiat, Fayez Tarsha Kurdi and Glenn Campbell

Remote Sens. 2022, 14(19), 4685; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194685 - 20 Sep 2022

Cited by 18 | Viewed by 3418

Abstract

Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more recent completed studies in the topography and surface feature identification domain. Four areas, with respect to the suggested approaches, have been analyzed and discussed: the input data, the concepts of point cloud structure for applying ML, the ML techniques used, and the applications of ML on LiDAR data. Then, an overview is provided to underline the advantages and the disadvantages of this research axis. Despite the training data labelling problem, the calculation cost, and the undesirable shortcutting due to data downsampling, most of the proposed methods use supervised ML concepts to classify the downsampled LiDAR data. Furthermore, despite the occasional highly accurate results, in most cases the results still require filtering. In fact, a considerable number of adopted approaches use the same data structure concepts employed in image processing to profit from available informatics tools. Knowing that the LiDAR point clouds represent rich 3D data, more effort is needed to develop specialized processing tools. Full article

(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

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