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Comparative investigation of parallel spatial interpolation algorithms for building large-scale digital elevation

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  • 1School of Engineering and Technology, China University of Geoscience (Beijing), Beijing, China.

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Summary

Choosing the right interpolation algorithm is key for accurate Digital Elevation Models (DEMs). This study compares Radial Basis Function (RBF), Moving Least Square (MLS), and Shepard

Keywords:
Digital elevation model (DEM)Geographic information system(GIS)Graphics processing unit (GPU)Moving least square (MLS)Parallel algorithmRadial basis function (RBF)Spatial interpolation

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Area of Science:

  • Geographic Information Science
  • Geomatics
  • Spatial Data Analysis

Background:

  • Large-scale Digital Elevation Model (DEM) creation is a critical task in geographic information science.
  • Interpolation algorithms significantly impact DEM accuracy and computational efficiency.
  • Algorithm selection must consider specific terrain characteristics for optimal performance.

Purpose of the Study:

  • To comparatively evaluate parallel Radial Basis Function (RBF)-based, Moving Least Square (MLS)-based, and Shepard's interpolation algorithms for DEM generation.
  • To analyze the influence of terrain type, data density, and distribution on interpolation accuracy and efficiency.

Main Methods:

  • Comparative performance analysis of RBF, MLS, and Shepard's interpolation algorithms.
  • Evaluation metrics included interpolation accuracy and computational efficiency.
  • Testing across diverse terrain types, data densities, and distribution patterns.

Main Results:

  • Significant differences in interpolation accuracy and computational efficiency were observed among the algorithms.
  • Terrain type, data density, and distribution patterns demonstrably influenced algorithm performance.
  • Specific algorithm suitability varied based on the evaluated factors.

Conclusions:

  • The findings provide guidance for selecting appropriate interpolation algorithms for large-scale DEM building.
  • Informed algorithm selection can optimize accuracy and efficiency in specific geographic contexts.
  • This research aids practitioners in making data-driven decisions for DEM creation.