Self-organizing GIS for solving problems of ecology and landscape studying Nikolay G. Markov, Alexandr A. Napryushkin Tomsk Polytechnical University, GIS laboratory, Tomsk, Russia

Self-organizing vector-raster GIS (SOVR GIS) solves the following tasks:  Preliminary processing of the received remote sensing (RS) data (solving tasks of projection transforming, geo- referencing, linear and nonlinear filtration, spectral and geometrical transformation)  Thematic processing of the processed RS data (automated interpretation)  Spatial analysis of the extracted thematic information represented in a vector format (complex quantitative estimations of the researched objects and phenomena)

Subsystem of preliminary processing Subsystem of self- organizing Subsystem of vector data visualization Interface shell of SOVR GIS Subsystem of interpretation and vectorization Subsystem of spatial analisys Subsystem of raster data visualization Data input- output subsystem Subsystem of 3D visualization Raster component Vector component Fig. 1. General structure of SOVR GIS

Thematic processing - the stage of extracting the geometric information from preliminary processed aerospace images. SOVR GIS provides the facilities for automatized extraction of thematic information from aerospace images.

Fig. 2. Automatized extraction of thematic information from aerospace images by means of SOVR GIS Kohonen’s neuronet classifier Preliminary processed aerospace image Vectorizing procedure Recognition procedure Self-organizing procedure Textural analysis procedure Cartographic sources Extended feature space Training data Vector thematic layers Spatial analysis Decisions

Self-organizing procedure (decision making algorithm) Non-parametric classifiers Advanced Bayesian classifier Extended feature space aerospace image Recognized landscape objects Fig.3 Self-organizing procedure

Fig. 4. Initial aerospace image of Tomsk-city (satellite RESURS-0, MSU-E scanner)

Fig. 5. Obtaining training data from a map

Fig. 6. Result of recognition. Red areas show the zones polluted with radioactive contaminants

Fig. 6. Mapping forest types of Tomsk region with SOVR GIS (satellite RESURS-0, MSU-E scanner) Initial aerospace image Map Cedar Pine tree Cedar+Fir Classified image

Self-organizing GIS for solving problems of ecology and landscape studying Nikolay G. Markov, Alexandr A. Napryushkin Tomsk Polytechnical University, GIS laboratory, Tomsk, Russia