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Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A Meta-Survey
Emil Dumic1, Luís A da Silva Cruz2,3
1Department of Electrical Engineering, University North, 104. Brigade 3, 42000 Varaždin, Croatia.
Sensors (Basel, Switzerland)
|April 28, 2025
Summary
This meta-survey reviews 3D point cloud (PC) applications in remote sensing (RS), datasets, and compression methods. It highlights trends and challenges for advancing PC use in RS.
Area of Science:
- Geospatial Science
- Computer Vision
Background:
- 3D point clouds (PC) are increasingly vital in remote sensing (RS).
- Existing reviews often focus on specific aspects, necessitating a consolidated overview.
Purpose of the Study:
- To comprehensively review 3D PC applications in RS.
- To survey essential datasets for R&D in RS.
- To analyze state-of-the-art PC compression methods.
Main Methods:
- Meta-survey synthesizing existing literature and original research.
- Categorization of PC applications in RS (specialized, precision agriculture, general).
- Survey of diverse PC datasets (urban, outdoor, indoor, vehicle, object, agriculture).
Main Results:
- Detailed overview of PC applications across various RS domains.
- Catalog of commonly used PC datasets for R&D.
- Analysis of compression techniques, from traditional to deep learning (DL)-based.
Conclusions:
- Identified emerging trends, challenges, and opportunities in PC for RS.
- Provides a valuable resource for researchers and practitioners.
- Emphasizes the need for continued advancements in PC processing and compression for RS.
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