Automated geoimage generation of Arctic industrial and natural complexes for regional management information support systems

• Published in: IOP Conference Series: Earth and Environmental Science
• Main Authors: Pilecki, B M, Vicentiy, A V
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2021
• Subjects: Arctic
• Online Access: http://dx.doi.org/10.1088/1755-1315/937/4/042079; https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042079; https://iopscience.iop.org/article/10.1088/1755-1315/937/4/042079/pdf

Abstract The problem of monitoring and management of industrial and natural complexes in the Arctic is an important problem, without solving which the effective development of the Arctic zones of the Russian Federation is impossible. Specialized information systems are actively used to support decision-making on planning and managing various types of activities in the Arctic, as well as the socio-economic development of the Arctic territories of the Russian Federation. Spatial information visualization tools are an important part of such systems. Visual representation of data is one of the most convenient types of information representation for human processing. This paper describes a method of automated geoimage generation for decision support systems based on the analysis of natural language texts. The use of such geoimages reduces the complexity of decision making for the management tasks of spatially distributed bio-socio-economic systems of the region. The paper describes the formulation of the research problem, review of geocoding tools and services, the original technology of automated geoimages (digital maps) generation based on the use of cloud geoservices. Particular attention is paid to the problem of recognition of named entities in natural language text. The recognition results are used as input data for geocoding and geovisualization process. As a result of the method, the text is converted into a geoimage (map) which can be used to support decision-making by a decision maker. In the final part of the paper, plans for further work are described to develop the capabilities of the automated geoimage generation method for decision support systems based on the analysis of natural language texts.