Big geo data surface approximation using radial basis functions: A comparative study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932155" target="_blank" >RIV/49777513:23520/17:43932155 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1016/j.cageo.2017.08.007" target="_blank" >http://dx.doi.org/10.1016/j.cageo.2017.08.007</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cageo.2017.08.007" target="_blank" >10.1016/j.cageo.2017.08.007</a>

Result language

angličtina

Original language name

Big geo data surface approximation using radial basis functions: A comparative study

Original language description

Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless methods for large scattered spatio-temporal vector data visualization</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers &amp; Geosciences

ISSN

0098-3004

e-ISSN

—

Volume of the periodical

109

Issue of the periodical within the volume

December 2017

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

51-58

UT code for WoS article

000415663200006

EID of the result in the Scopus database

2-s2.0-85027709533