The article proposes a solution to one of the key problems – the use of information technology in the management of territorial processes based on the development of tools for digitalization of economic processes on the basis of the software product to identify the structural elements of the economic potential of the regions of Russia, including the features of such regions as the Arctic. As a tool for the implementation of this approach, the VBA programming language built into the Excel spreadsheet is used, which allows flexible use of a standard software product for solving problems at the macroeconomic level. The aim of the study is to develop tools for digitalization of economic processes on the basis of a computer program to determine the extreme structural elements of the growth of the economic potential of the regions to support management decision-making. The systematic approach, methods of statistical and economic analysis, including horizontal and vertical analysis, allowed the authors to assess the dynamics of extreme values of the level of development of economic sectors in the regions of Russia and to develop recommendations for the development of the Federal districts and regions. The directions of further researches consist in application of multidimensional modeling of data for collecting and automatic processing of statistical information on a condition of subjects of the Arctic region.

The article shows that Russia regulates air and forest protection and conservation using national projects, e.g. the Ecology Project. A comparative analysis of relative indicators calculated using the suggested methods helped rank each region by the criteria of management of and contamination in three environmental components: atmosphere (air), hydrosphere (water), and forests (plants). The findings of the comparative analysis carried out for the regions to assess the condition of their environmental components, pollution levels, and conservation spending make it possible to determine strategic areas of conservation efforts for different planning horizons.

The transformation of the development of the Arctic is due to modern robotic systems. The use of unmanned vehicles in many industries in the Arctic provides an array of photo and video information. Accuracy of image analysis and pattern recognition is enhanced by image preprocessing. However, the existing binarization algorithms are not universal for images with different distortions and loss of information. The accuracy of binarization algorithms depends on many factors, such as shadows, uneven lighting, low contrast, noise, etc. Images with different characteristics of light and noise are simulated in order to model various lighting conditions on information from digital cameras of robotic systems. The paper investigates global and adaptive image binarization algorithms. The binarized images were obtained using these algorithms and the results of binarized images recognition are compared by an optical character recognition system. An analysis of the comparison results showed that for images made in poor lighting conditions or images with low contrast, or images with high noise levels, adaptive binarization algorithms are better suited. However, in most cases it is not possible to obtain fully correctly recognized images.The paper proposes a new binarization method based on artificial neural networks. The process of creating an artificial neural network is shown, include the parameters for determining the class of a pixel, the adjustment of weights, the architecture of an artificial neural network. A comparison of the proposed artificial neural network with existing image binarization algorithms demonstrate that in most cases the artificial neural network has the result of image processing at the level of adaptive algorithms or higher. The proposed method of images binarization based on the image color characteristics analysis allows to solve image recognition tasks by robotized systems.

The article provides a comparative digital maturity assessment of two leading petroleum companies licensed for Arctic offshore exploration and production (PAO Gazprom Neft and PAO Rosneft) in a ‘hardware-software-hardware’ succession as well as a comparative analysis with leading practices in offshore project logistics. It identifies approximate cost-cutting limits of digital transformation in Arctic projects and factors limiting cost efficiency of offshore projects in the Russian Arctic. The goal of the study is to identify a potential increase in digital maturity and scope of digital transformation in the Arctic project supply chain in Russia. The study is based on modern approaches to sustainable supply chain management in oil industry and supply chain streamlining for offshore projects. The article identifies a group of factors limiting cost efficiency of Arctic projects, specific to the Russian Arctic. Conclusions and recommendations: the study has shown that the main factor limiting digital transformation of offshore projects in the Russian Arctic is the increasing technological underperformance of oil service companies. To build capacity in digital maturity of the offshore project supply chain, it is necessary to focus on disruptive technologies that could significantly lower the cost of oil production in the Arctic area.

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