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New Perspectives on 3D Point Cloud II

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 6397

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Dr. Sara Gonizzi Barsanti

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Department of Engineering, Università Degli Studi Della Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
Interests: photogrammetry; geomatics; surveying; topography; 3D modeling; reverse engineering; finite element analysis; geographic information system; cultural heritage; BIM; HBIM; VR/AR/XR
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Dear Colleagues,

A point cloud is a database of points that represent a physical object or environment. The application areas of 3D point cloud processing are expanding not only in the frame of geospatial analysis, but also in civil engineering and manufacturing, transport and city planning, construction, geology, ecology, forestry, mechanical engineering, and so on.

Their use is not only related to the creation of 3D meses or models of a surveyed object, but is expanding towards the possibility of classification, shape detection, and data extraction via automated procedures. The process of semantic segmentation and feature extraction can be used for preservation, the prediction of events, climate control, and as a basis for decision making and analysis.

This is a hot topic in research due to its innovation possibilities and multiple potential application fields, not to mention the problems that have not yet found a definitive solution (Poux, F., 2019). Furthermore, the possibility of using and developing open-source applications for data management and processing makes this topic a key area in the research.

This Special Issue aims to collect original papers regarding innovative processing and applications of 3D point clouds acquired from remote sensing sensors in different and new areas of research (i.e., forest, environment, cultural heritage, geology, maritime, architecture, climate analysis, and city modeling research). Great importance will be given to new, innovative research regarding open-source solutions, as well as to the creation and definition of automated/semi-automated procedures to collect, post-process, classify, and use 3D point clouds.

Topics should be strictly related to the aims of the Remote Sensing journal, with data results from different sensors of 3D reality-based surveys.

The papers suitable for this Special Issue must emphasize new perspectives and innovative methods on 3D point cloud acquisition, analysis, and post-processing, focusing on data extraction, semantic feature extraction, machine and deep learning algorithms, AI, the use of segmented point clouds for HBIM and BIM, and automated and semi-automated procedures. Importance should also be given to multi-temporal point clouds and the characterization of object dynamics.

Dr. Sara Gonizzi Barsanti
Guest Editor

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

point cloud acquisition (laser scanner, mono/stereo vision, panoramas, phone cameras, aerial and satellite images, indoor and narrow spaces)
deep learning for segmentation/semantic representation and processing
AI/ML/DL for point cloud processing and pattern recognition
SCAN-to-BIM/HBIM
data extraction/data analysis
point cloud registration
multi-temporal point clouds
fusion of point clouds from different sensors

Related Special Issue

New Perspectives on 3D Point Cloud in Remote Sensing (9 articles)

Published Papers (6 papers)

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Research

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32 pages, 16649 KiB

Open AccessArticle

Identifying Conservation Introduction Sites for Endangered Birds through the Integration of Lidar-Based Habitat Suitability Models and Population Viability Analyses

by Erica Marie Gallerani, Lucas Berio Fortini, Christopher C. Warren and Eben H. Paxton

Remote Sens. 2024, 16(4), 680; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16040680 - 14 Feb 2024

Viewed by 1088

Abstract

Similar to other single-island endemic Hawaiian honeycreepers, the critically endangered ‘ākohekohe (Palmeria dolei) is threatened by climate-driven disease spread. To avert the imminent risk of extinction, managers are considering novel measures, including the conservation introduction (CI) of ‘ākohekohe from Maui to higher elevation habitats on the Island of Hawai’i. This study integrated lidar-based habitat suitability models (LHSMs) and population viability analyses (PVAs) to assess five candidate sites currently considered by managers for CI. We first developed an LHSM for the species’ native range on Maui. We then projected habitat suitability across candidate CI sites, using forest structure and topography metrics standardized across sensor types. Given the structural variability observed within the five candidate sites, we identified clusters of contiguous, highly suitable habitat as potential release sites. We then determined how many adult individuals could be supported by each cluster based on adult home range estimates. To determine which clusters could house the minimum number of ‘ākohekohe birds necessary for a stable or increasing future population, we conducted PVAs under multiple scenarios of bird releases. We found that canopy height and relative height 90 had the greatest effects on model performance, possibly reflecting ‘ākohekohe’s preference for taller canopies. We found that a small release of at least nine pairs of equal sex ratios were sufficient for an 80% chance of success and a <1% chance of extirpation in 20 years, resulting in a minimum release area of 4.5 ha in size. We integrated the results of the LHSM and PVA into an interactive web application that allowed managers to consider the caveats and uncertainties associated with both LHSMs and PVAs in their decision-making process. As climate change continues to threaten species worldwide, this research demonstrates the value of lidar remote sensing combined with species-specific models to enable rapid, quantitative assessments that can inform the increasing consideration of time-sensitive conservation introductions. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud II)

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23 pages, 11832 KiB

Open AccessArticle

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

by Jingxue Wang, Dongdong Zang, Jinzheng Yu and Xiao Xie

Remote Sens. 2024, 16(1), 190; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010190 - 02 Jan 2024

Viewed by 921

Abstract

Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud II)

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22 pages, 8881 KiB

Open AccessArticle

Evaluation of Open Geotechnical Knowledge in Urban Environments for 3D Modelling of the City of Seville (Spain)

by Cristina Soriano-Cuesta, Rocío Romero-Hernández, Emilio J. Mascort-Albea, Martin Kada, Andreas Fuls and Antonio Jaramillo-Morilla

Remote Sens. 2024, 16(1), 141; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010141 - 28 Dec 2023

Viewed by 679

Abstract

The need for sustainable urban growth management and preventive conservation of built elements constitute the key factors in today’s increasing demand for the better understanding of subsoil. This information, mainly available from geotechnical surveys, can be integrated into spatial databases to produce operational models. Aiming to generate strategies that enable the visualisation of underground properties in highly anthropised environments, the following four-phase methodology has been proposed: (a) Gathering of geotechnical data; (b) Spatial and statistical analysis; (c) Database design; (d) Generation of 2D and 3D models. Following the aforementioned criteria and using open sources, a spatial dataset of 650 points located within the historical centre of Seville (Spain) has been developed. This urban area is characterised by the heterogeneous distribution of its soil layers and their geotechnical properties. The results show that the application of this method enables a prompt and efficient display of the distribution of geotechnical layers in urban and metropolitan environments, by considering the variations in their mechanical properties. This simplified approach therefore establishes a new starting point for the development of predictive strategies based on approaches of a more complex nature that facilitate the analysis of the interactions between subsoil, buildings, and infrastructures. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud II)

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20 pages, 5932 KiB

Open AccessArticle

Color-Based Point Cloud Classification Using a Novel Gaussian Mixed Modeling-Based Approach versus a Deep Neural Network

by Martin Štroner, Rudolf Urban and Lenka Línková

Remote Sens. 2024, 16(1), 115; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16010115 - 27 Dec 2023

Cited by 1 | Viewed by 827

Abstract

The classification of point clouds is an important research topic due to the increasing speed, accuracy, and detail of their acquisition. Classification using only color is basically absent in the literature; the few available papers provide only algorithms with limited usefulness (transformation of three-dimensional color information to a one-dimensional one, such as intensity or vegetation indices). Here, we proposed two methods for classifying point clouds in RGB space (without using spatial information) and evaluated the classification success since it allows a computationally undemanding classification potentially applicable to a wide range of scenes. The first is based on Gaussian mixture modeling, modified to exploit specific properties of the RGB space (a finite number of integer combinations, with these combinations repeated in the same class) to automatically determine the number of spatial normal distributions needed to describe a class (mGMM). The other method is based on a deep neural network (DNN), for which different configurations (number of hidden layers and number of neurons in the layers) and different numbers of training subsets were tested. Real measured data from three sites with different numbers of classified classes and different “complexity” of classification in terms of color distinctiveness were used for testing. Classification success rates averaged 99.0% (accuracy) and 96.2% (balanced accuracy) for the mGMM method and averaged 97.3% and 96.7% (balanced accuracy) for the DNN method in terms of the best parameter combinations identified. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud II)

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23 pages, 7487 KiB

Open AccessArticle

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

by Xiaoliang Meng, Tianyi Wang, Dayu Cheng, Wensong Su, Peng Yao, Xiaoli Ma and Meizhen He

Remote Sens. 2023, 15(20), 5006; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15205006 - 18 Oct 2023

Viewed by 1105

Abstract

The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud II)

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Other

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15 pages, 4640 KiB

Open AccessTechnical Note

Selection of an Algorithm for Assessing the Verticality of Complex Slender Objects Using Semi-Automatic Point Cloud Analysis

by Wojciech Matwij, Tomasz Lipecki and Wojciech Franciszek Jaśkowski

Remote Sens. 2024, 16(3), 435; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16030435 - 23 Jan 2024

Viewed by 553

Abstract

Remote technologies, including laser scanning, are frequently employed to acquire data describing the geometric condition of engineering objects. The automation of point cloud processing becomes essential for promptly and reliably monitoring changes in their current shape. The article introduces a methodology for generating point clouds, focusing on detecting the shape of the object’s cross profiles and subsequently determining its inclination through simulations and real data recorded using terrestrial laser scanning technology. The simulations enabled the identification of variations in the characteristics of changes in the course of the axis of a slender structure, depending on the adopted calculation method. Point clouds derived from measurements of complex engineering objects facilitated the validation of the assumptions of the proposed methodology. The suggested solution enables the semi-automatic extraction of data from point clouds and the assessment of the geometric state of engineering object axes based on multi-temporal point clouds. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud II)

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