Abstract
The article is devoted to solving the scientific problem of accumulating and systematizing models and machine learning algorithms by developing a repository of deep neural network models for analyzing and predicting of spatial processes in order to support the process of making managerial decisions in the field of ensuring conditions for sustainable development of regions. The issues of architecture development and software implementation of a repository of deep neural network models for spatial data analysis are considered, based on a new ontological model, which makes it possible to systematize models in terms of their application for solving design problems. An ontological model of a deep neural network repository for spatial data analysis is decomposed into the domain of deep machine learning models, problems being solved and data. Special attention is paid to the problems of storing data in the repository and the development of a subsystem for visualizing neural networks using a graph model. The authors have shown that for organizing a repository of deep neural network models, it is advisable to use a scientifically grounded set of database management systems integrated into a multi-model storage, characterizing the domains of using relational and NoSQL storages.
Highlights
A significant role in solving the problem of strengthening the connectivity of territories of countries and regions is played by the introduction of effective digital infrastructures of spatial data (SDI), aimed at operational diagnostics of natural-socialproduction systems (NSPS) and high-precision forecasting of the development of natural processes and phenomena [1]
Analysis of the strengths and weaknesses of the currently existing repositories of deep neural network models of general purpose (AWS Marketplace, Open Neural Network Exchange, Wolfram Neural Net Repository and others) [15] made it possible to form a list of specific problems, the solution of which will ensure the creation of an effective system suitable for solving specific practical oriented tasks in the field of spatial data analysis
An ontological model of a deep neural network repository for spatial data analysis can be decomposed into domains of deep machine learning models, tasks to be solved, and data (Fig. 1)
Summary
A significant role in solving the problem of strengthening the connectivity of territories of countries and regions is played by the introduction of effective digital infrastructures of spatial data (SDI), aimed at operational diagnostics of natural-socialproduction systems (NSPS) and high-precision forecasting of the development of natural processes and phenomena [1]. The subject of analysis can be space imagery data, aerial photography, information arrays about natural, social and economic objects with a distributed geospatial organization [2]. The development of the federal spatial data infrastructure is necessary in order to effectively solve the problem of remote monitoring of mobile objects and geographically distributed resources in order to ensure information connectivity of countries and create conditions for sustainable development of the country [5]. The functioning of spatial data infrastructures should be based on and application of new effective methods, approaches and algorithms for the analysis of spatio-temporal data, which can function both on the basis of classical hard and soft computations based on the complex application of fuzzy logic, neural network models, evolutionary modeling [6]
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