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Updated: May 10, 2025

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Three-Dimensional Point Cloud Applications, Datasets, and Compression Methodologies for Remote Sensing: A

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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
point cloudpoint cloud compressionpoint cloud datasetsremote sensing

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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.