AI Article Synopsis
- The paper explores how different interpolation algorithms affect the accuracy and efficiency of creating Digital Elevation Models (DEMs) in geographic information science.
- It compares the performance of three specific algorithms: parallel Radial Basis Function (RBF)-based, Moving Least Square (MLS)-based, and Shepard's interpolation.
- The study evaluates how factors like terrain type, data density, and distribution impact the choice of algorithm, aiming to guide users in selecting the best method for their specific needs.
Article Abstract
The building of large-scale Digital Elevation Models (DEMs) using various interpolation algorithms is one of the key issues in geographic information science. Different choices of interpolation algorithms may trigger significant differences in interpolation accuracy and computational efficiency, and a proper interpolation algorithm needs to be carefully used based on the specific characteristics of the scene of interpolation. In this paper, we comparatively investigate the performance of parallel Radial Basis Function (RBF)-based, Moving Least Square (MLS)-based, and Shepard's interpolation algorithms for building DEMs by evaluating the influence of terrain type, raw data density, and distribution patterns on the interpolation accuracy and computational efficiency. The drawn conclusions may help select a suitable interpolation algorithm in a specific scene to build large-scale DEMs.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924418 | PMC |
| http://dx.doi.org/10.7717/peerj-cs.263 | DOI Listing |
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