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Remote Sensing
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Point Cloud Data Analysis and Applications
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Point Cloud Data Analysis and Applications

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1396

Special Issue Editors

Dr. Ruijin Ma

E-Mail Website
Guest Editor
MS GIS Program, University of Redlands, Redlands, CA, USA
Interests: photogrammetry; GIS; remote sensing; lidar
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
Dr. Maolin Chen

E-Mail Website
Guest Editor
School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China
Interests: multi-source LiDAR data fusion and classification; 3D modeling & reconstruction; LiDAR-based environmental monitoring

Special Issue Information

Dear Colleagues,

With the advancement of technologies and methods in point cloud data acquisition through laser scanning, photogrammetry, and even RADAR technology, coupled with the development of analysis methods, point clouds have become an important and essential geographic data representing real-world objects and environments. With its rich information, point cloud data have been used in many applications in fields like surveying, forestry, geospatial digital twins, digital surface modeling, robotics, 3D city modeling applications, and autonomous driving. However, point cloud data analyses still face challenges in data processing, interpretation, and management due to its un-ordered data structure, large data volume, and data inconsistency across platforms.

This Special Issue aims to highlight the latest research in point cloud processing and analysis, including data filtering, semantic segmentation, object recognition, point cloud registration, and data fusion to address the challenges in 3D scene understanding, geospatial digital twins modeling, and environment re-structuring.

This Special Issue focuses on the state-of-the-art methodologies and applications in 3D point cloud processing for scene understanding and modeling. Its scope aligns with the broader goals of remote sensing research, highlighting pioneering approaches to point segmentation, multi-modal data fusion, and modeling.

We invite submissions on a variety of topics of point cloud processing, including but not limited to the following:

  • Segmentation of point clouds;
  • Object detection;
  • Multi-sensor/multi-platform data fusion;
  • Point cloud registration;
  • Innovative applications of point clouds in digital twin, forest inventory, and environmental monitoring;
  • Large-scale point clouds processing and management.

Dr. Ruijin Ma
Prof. Dr. Tarig Ali
Dr. Maolin Chen
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

  • point clouds processing
  • geospatial digital twin modeling
  • data fusion

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Published Papers (2 papers)

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Research

31 pages, 6007 KB  
Article
Geometry and Topology Preservable Line Structure Construction for Indoor Point Cloud Based on the Encoding and Extracting Framework
by Haiyang Lyu, Hongxiao Xu, Donglai Jiao and Hanru Zhang
Remote Sens. 2025, 17(17), 3033; https://doi.org/10.3390/rs17173033 - 1 Sep 2025
Viewed by 255
Abstract
The line structure is an efficient form of representation and modeling for LiDAR point clouds, while the Line Structure Construction (LSC) method aims to extract complete and coherent line structures from complex 3D point clouds, thereby providing a foundation for geometric modeling, scene [...] Read more.
The line structure is an efficient form of representation and modeling for LiDAR point clouds, while the Line Structure Construction (LSC) method aims to extract complete and coherent line structures from complex 3D point clouds, thereby providing a foundation for geometric modeling, scene understanding, and downstream applications. However, traditional LSC methods often fall short in preserving both the geometric integrity and topological connectivity of line structures derived from such datasets. To address this issue, we propose the Geometry and Topology Preservable Line Structure Construction (GTP-LSC) method, based on the Encoding and Extracting Framework (EEF). First, in the encoding phase, point cloud features related to line structures are mapped into a high-dimensional feature space. A 3D U-Net is then employed to compute Subsets with Structure feature of Line (SSL) from the dense, unstructured, and noisy indoor LiDAR point cloud data. Next, in the extraction phase, the SSL is transformed into a 3D field enriched with line features. Initially extracted line structures are then constructed based on Morse theory, effectively preserving the topological relationships. In the final step, these line structures are optimized using RANdom SAmple Consensus (RANSAC) and Constructive Solid Geometry (CSG) to ensure geometric completeness. This step also facilitates the generation of complex entities, enabling an accurate and comprehensive representation of both geometric and topological aspects of the line structures. Experiments were conducted using the Indoor Laser Scanning Dataset, focusing on the parking garage (D1), the corridor (D2), and the multi-room structure (D3). The results demonstrated that the proposed GTP-LSC method outperformed existing approaches in terms of both geometric integrity and topological connectivity. To evaluate the performance of different LSC methods, the IoU Buffer Ratio (IBR) was used to measure the overlap between the actual and constructed line structures. The proposed method achieved IBR scores of 92.5% (D1), 94.2% (D2), and 90.8% (D3) for these scenes. Additionally, Precision, Recall, and F-Score were calculated to further assess the LSC results. The F-Score of the proposed method was 0.89 (D1), 0.92 (D2), and 0.89 (D3), demonstrating superior performance in both visual analysis and quantitative results compared to other methods. Full article
(This article belongs to the Special Issue Point Cloud Data Analysis and Applications)
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Graphical abstract

24 pages, 6742 KB  
Article
Low-Overlap Registration of Multi-Source LiDAR Point Clouds in Urban Scenes Through Dual-Stage Feature Pruning and Progressive Hierarchical Methods
by Kaifeng Ma, Fengtao Yan, Shiming Li, Guiping Huang, Xiaojie Jia, Feng Wang and Li Chen
Remote Sens. 2025, 17(17), 2938; https://doi.org/10.3390/rs17172938 - 24 Aug 2025
Viewed by 593
Abstract
With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds in capturing intricate structures within complex scenes, combined with the [...] Read more.
With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds in capturing intricate structures within complex scenes, combined with the computational burden imposed by large datasets, presents substantial challenges to current registration methods. The proposed method encompasses two innovative feature point pruning techniques and two closely interconnected progressive processes. First, it identifies structural points that effectively represent the features of the scene and performs a rapid initial alignment of point clouds within the two-dimensional plane. Subsequently, it establishes the mapping relationship between the point clouds to be matched utilizing FPFH descriptors, followed by further screening to extract the maximum consensus set composed of points that meet constraints based on the intensity of graph nodes. Then, it integrates the processes of feature point description and similarity measurement to achieve precise point cloud registration. The proposed method effectively extracts matching primitives from large datasets, addressing issues related to false matches and noise in complex data environments. It has demonstrated favorable matching results even in scenarios with low overlap between datasets. On two public datasets and a self-constructed dataset, the method achieves an effective point set screening rate of approximately 1‰. On the WHU-TLS dataset, our method achieves a registration accuracy characterized by a rotation precision of 0.062° and a translation precision of 0.027 m, representing improvements of 70% and 80%, respectively, over current state-of-the-art (SOTA) methods. The results obtained from real registration tasks demonstrate that our approach attains competitive registration accuracy when compared with existing SOTA techniques. Full article
(This article belongs to the Special Issue Point Cloud Data Analysis and Applications)
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