Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey
This meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods. It offers a comprehensive exploration of the diverse applications of...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1660 |
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| author | Emil Dumic Luís A. da Silva Cruz |
| author_facet | Emil Dumic Luís A. da Silva Cruz |
| author_sort | Emil Dumic |
| collection | DOAJ |
| description | This meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods. It offers a comprehensive exploration of the diverse applications of point clouds in remote sensing, including specialized tasks within the field, precision agriculture-focused applications, and broader general uses. Furthermore, datasets that are commonly used in remote-sensing-related research and development tasks are surveyed, including urban, outdoor, and indoor environment datasets; vehicle-related datasets; object datasets; agriculture-related datasets; and other more specialized datasets. Due to their importance in practical applications, this article also surveys point cloud compression technologies from widely used tree- and projection-based methods to more recent deep learning (DL)-based technologies. This study synthesizes insights from previous reviews and original research to identify emerging trends, challenges, and opportunities, serving as a valuable resource for advancing the use of point clouds in remote sensing. |
| format | Article |
| id | doaj-art-871f044775a94d28bfe8bde7321c5384 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-871f044775a94d28bfe8bde7321c53842025-08-20T02:43:06ZengMDPI AGSensors1424-82202025-03-01256166010.3390/s25061660Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-SurveyEmil Dumic0Luís A. da Silva Cruz1Department of Electrical Engineering, University North, 104. Brigade 3, 42000 Varaždin, CroatiaDepartment of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalThis meta-survey provides a comprehensive review of 3D point cloud (PC) applications in remote sensing (RS), essential datasets available for research and development purposes, and state-of-the-art point cloud compression methods. It offers a comprehensive exploration of the diverse applications of point clouds in remote sensing, including specialized tasks within the field, precision agriculture-focused applications, and broader general uses. Furthermore, datasets that are commonly used in remote-sensing-related research and development tasks are surveyed, including urban, outdoor, and indoor environment datasets; vehicle-related datasets; object datasets; agriculture-related datasets; and other more specialized datasets. Due to their importance in practical applications, this article also surveys point cloud compression technologies from widely used tree- and projection-based methods to more recent deep learning (DL)-based technologies. This study synthesizes insights from previous reviews and original research to identify emerging trends, challenges, and opportunities, serving as a valuable resource for advancing the use of point clouds in remote sensing.https://www.mdpi.com/1424-8220/25/6/1660point cloudremote sensingpoint cloud datasetspoint cloud compression |
| spellingShingle | Emil Dumic Luís A. da Silva Cruz Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey Sensors point cloud remote sensing point cloud datasets point cloud compression |
| title | Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey |
| title_full | Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey |
| title_fullStr | Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey |
| title_full_unstemmed | Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey |
| title_short | Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey |
| title_sort | three dimensional point cloud applications datasets and compression methodologies for remote sensing a meta survey |
| topic | point cloud remote sensing point cloud datasets point cloud compression |
| url | https://www.mdpi.com/1424-8220/25/6/1660 |
| work_keys_str_mv | AT emildumic threedimensionalpointcloudapplicationsdatasetsandcompressionmethodologiesforremotesensingametasurvey AT luisadasilvacruz threedimensionalpointcloudapplicationsdatasetsandcompressionmethodologiesforremotesensingametasurvey |