Automatic Mapping and Classification of Spatial Environmental Data

Details

Serval ID
serval:BIB_A28FA12CE344
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Automatic Mapping and Classification of Spatial Environmental Data
Journal
Geocomputation, Sustainability and Environmental Planning
Author(s)
Kanevski M., Timonin V., Pozdnoukhov A .
ISSN
978-3-642-19733-8
ISSN-L
1860-949X
Publication state
Published
Issued date
2011
Peer-reviewed
Oui
Volume
348
Pages
205-223
Language
english
Notes
Kanevski2011
Abstract
The paper deals with the development and application of the generic
methodology for automatic processing (mapping and classification)
of environmental data. General Regression Neural Network (GRNN) is
considered in detail and is proposed as an efficient tool to solve
the problem of spatial data mapping (regression). The Probabilistic
Neural Network (PNN) is considered as an automatic tool for spatial
classifications. The automatic tuning of isotropic and anisotropic
GRNN/PNN models using cross-validation procedure is presented. Results
are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm
using independent validation data set. Real case studies are based
on decision-oriented mapping and classification of radioactively
contaminated territories.
Keywords
Automatic cartography, General Regression, Neural Networks, Probabilistic, Neural Networks, decision-oriented mapping, classification
Create date
25/11/2013 17:18
Last modification date
20/08/2019 15:08
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