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.