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About the Authors

Alexey A. Kolesnikov

Siberian State University of Geosystems and Technologies,
Plakhotnogo str., 10, 630108, Novosibirsk, Russia,

Pavel M. Kikin

Siberian State University of Geosystems and Technologies,
Plakhotnogo str., 10, 630108, Novosibirsk, Russia,

Elena V. Komissarova

Siberian State University of Geosystems and Technologies,
Plakhotnogo str., 10, 630108, Novosibirsk, Russia,

Elena L. Kasyanova

Siberian State University of Geosystems and Technologies,
Plakhotnogo str., 10, 630108, Novosibirsk, Russia,


Computer vision and data analysis are one of the most popular topics both among information technologies and most areas of scientific research. Geography, cartography and geoinformatics with their variety of types of source data, spatial analysis problems, visualization methods, modeling and forecasting methods cannot be better suited for using modern algorithms of machine learning. However, the use of these technologies rarely goes beyond the solution of private tasks of commercial campaigns and, often, is not widely publicized and any systematization or scientific description. In this respect, we decided to make a research of machine learning technologies in the context of using it while solving the most typical problems of geographical research. The classification of problems, algorithms and methods of computer vision from the point of view of geoinformation systems is given. Possible ways of solving some problems of classification and segmentation of raster images are described. The most popular of them are analyzed, including such as the use of convolutional and pre-conditioned neural networks for the recognition of objects on satellite images. The approbation took place within the competition in vectorization of hydrographic objects and the classification of objects in the open sea Statoil/C-CORE Iceberg Classifier Challenge. As initial data, we took marked satellite images of the water surface. The ways of spatial data analysis using the Moran index and calculating the Gini coefficient are considered. The methods of predicting the location of the coordinates of the house and work sudden user using the time series of ATM and cash register transactions at service points using regression algorithms were investigated. To conduct this study, the data set of the All-Russian competition in machine learning Raiffeisen Data Cup was used. We compared the results of usage of the machine learning algorithms and traditional methods of spatial analysis. Based on the results of the fulfilled investigations, we made the conclusions about the usability of the algorithms and technologies for specific geographic tasks, taking into account the dependence of the results from the types of using data used, resources requirements, accuracy, and universality.


machine learning, cartography, segmentation, neural networks, regression, geoinformatics, aerial images.


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For citation: Kolesnikov A.A., Kikin P.M., Komissarova E.V., Kasyanova E.L. USE OF MACHINE LEARNING TECHNOLOGIES IN DECISION OF GEOINFORMATIONAL TASKS Proceedings of the International conference “InterCarto. InterGIS”. 2018;24(2):371–384