Special Issue "Advanced Research Based on Multi-Dimensional Point Cloud Analysis"

Special Issue Editors

Prof. Dr. Ing. Greet Deruyter
Website
Guest Editor
Ghent University, Sint-Pietersnieuwstraat 41 B2, B-9000 Gent, Belgium
Interests: 3D data acquisition, laser scanning, photogrammetry, GIS, risk assessment, flooding, modelling, land surveying
Prof. Dr. Ing. Ansgar Brunn
Website
Guest Editor
University Wuerzburg-Schweinfurt (FHWS), Roentgenring 8, D-97070 Wuerzburg, Germany
Interests: photogrammetry, laser scanning, 3D point clouds, remote sensing, spatial analysis, online education

Special Issue Information

Because of the evolution of data acquisition techniques together with the availability of more powerful computers and software, the use of so-called point clouds for advanced research on the geometry and spectral characteristics of objects and the object space becomes more and more generally accepted. As point clouds are generated by different data acquisition methods, such as terrestrial laser scanning, mobile mapping, multibeam, photogrammetry, etc., the application domain is also diverse.

This Special Issue aims to collect and share innovative research that reflects the diversity of research based on point clouds, starting from the multi-dimensional point clouds for analyses, interpretation, improvement and more. The research domains can be, but are not limited to
  • bathymetry,
  • erosion,
  • static and mobile laser scanning,
  • mobile mapping,
  • photogrammetry,
  • creation of DTM and DSM,
  • classification based on spectral or radiometric characteristics,
  • deformation measurements, and
  • other applications of point clouds.

Prof. Dr. Ing. Greet Deruyter
Prof. Dr. Ing. Ansgar Brunn
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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 clouds
  • Bathymetry
  • Laser scanning
  • Photogrammetry
  • Mobile mapping
  • Modelling
  • Spatial analysis
  • Deformation assessment
  • Industry 4.0
  • Autonomous Driving
  • SLAM
  • Agriculture

Published Papers (4 papers)

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Research

Open AccessArticle
Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds
ISPRS Int. J. Geo-Inf. 2020, 9(10), 595; https://doi.org/10.3390/ijgi9100595 - 10 Oct 2020
Abstract
Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and [...] Read more.
Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Open AccessArticle
Utilizing Airborne LiDAR and UAV Photogrammetry Techniques in Local Geoid Model Determination and Validation
ISPRS Int. J. Geo-Inf. 2020, 9(9), 528; https://doi.org/10.3390/ijgi9090528 - 02 Sep 2020
Abstract
This investigation evaluates the performance of digital terrain models (DTMs) generated in different vertical datums by aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry techniques, for the determination and validation of local geoid models. Many engineering projects require the point heights referring to [...] Read more.
This investigation evaluates the performance of digital terrain models (DTMs) generated in different vertical datums by aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry techniques, for the determination and validation of local geoid models. Many engineering projects require the point heights referring to a physical surface, i.e., geoid, rather than an ellipsoid. When a high-accuracy local geoid model is available in the study area, the physical heights are practically obtained with the transformation of global navigation satellite system (GNSS) ellipsoidal heights of the points. Besides the commonly used geodetic methods, this study introduces a novel approach for the determination and validation of the local geoid surface models using photogrammetry. The numeric tests were carried out in the Bergama region, in the west of Turkey. Using direct georeferenced airborne LiDAR and indirect georeferenced UAV photogrammetry-derived point clouds, DTMs were generated in ellipsoidal and geoidal vertical datums, respectively. After this, the local geoid models were calculated as differences between the generated DTMs. Generated local geoid models in the grid and pointwise formats were tested and compared with the regional gravimetric geoid model (TG03) and a high-resolution global geoid model (EIGEN6C4), respectively. In conclusion, the applied approach provided sufficient performance for modeling and validating the geoid heights with centimeter-level accuracy. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Open AccessArticle
Nighttime Mobile Laser Scanning and 3D Luminance Measurement: Verifying the Outcome of Roadside Tree Pruning with Mobile Measurement of the Road Environment
ISPRS Int. J. Geo-Inf. 2020, 9(7), 455; https://doi.org/10.3390/ijgi9070455 - 19 Jul 2020
Abstract
Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial [...] Read more.
Roadside vegetation can affect the performance of installed road lighting. We demonstrate a workflow in which a car-mounted measurement system is used to assess the light-obstructing effect of roadside vegetation. The mobile mapping system (MMS) includes a panoramic camera system, laser scanner, inertial measurement unit, and satellite positioning system. The workflow and the measurement system were applied to a road section of Munkkiniemenranta, Helsinki, Finland, in 2015 and 2019. The relative luminance distribution on a road surface and the obstructing vegetation were measured before and after roadside vegetation pruning applying a luminance-calibrated mobile mapping system. The difference between the two measurements is presented, and the opportunities provided by the mobile 3D luminance measurement system are discussed. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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Open AccessArticle
Sharp Feature Detection as a Useful Tool in Smart Manufacturing
ISPRS Int. J. Geo-Inf. 2020, 9(7), 422; https://doi.org/10.3390/ijgi9070422 - 30 Jun 2020
Abstract
Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods [...] Read more.
Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods for digitization and object reconstruction, especially in the area of reverse engineering. Commonly used scanning software based on well-known algorithms can correctly process smooth objects. Nevertheless, they are usually not applicable for complex-shaped models with sharp features. The number of the points on the edges is extremely limited due to the principle of laser scanning and sometimes also low scanning resolution. Therefore, a correct edge reconstruction problem occurs. The same problem appears in many other laser scanning applications, i.e., in the representation of the buildings from airborne laser scans for 3D city models. We focus on a method for preservation and reconstruction of sharp features. We provide a detailed description of all three key steps: point cloud segmentation, edge detection, and correct B-spline edge representation. The feature detection algorithm is based on the conventional region-growing method and we derive the optimal input value of curvature threshold using logarithmic least square regression. Subsequent edge representation stands on the iterative algorithm of B-spline approximation where we compute the weighted asymmetric error using the golden ratio. The series of examples indicates that our method gives better or comparable results to other methods. Full article
(This article belongs to the Special Issue Advanced Research Based on Multi-Dimensional Point Cloud Analysis)
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