Formation of a smart city model based on the dynamics of changes in urban building
DOI:
https://doi.org/10.30837/2522-9818.2025.2.016Keywords:
set-theoretic model; remote sensing data; deep learning, updating data; directions of city intellectualization.Abstract
The purpose of the article is improving the validity of assessments for the formation of decisions on the intellectualization of the city based on analysis of urban building. Objectives. The trends in the development of Smart City concepts were analyzed; requirements for Smart Cities are analyzed and a set-theoretical model of a Smart City is proposed; the method of analyzing urban building using remote sensing data has been improved and the possibility of its use has been experimentally confirmed. The following methods used are – methods of system analysis, set theory, and deep learning. The following results were obtained. Creating a comfortable urban environment promotes economic growth, improves the quality of life and ensures sustainable development. This is possible through the implementation of the smart city concept. To unify the steps to create Smart Cities, a set-theoretic model of a Smart City is proposed. It generalizes the requirements of standards of the ISO 37100 series and combines a set of indicators, ensuring the implementation of certain areas of intellectualization. The choice of the intellectualization direction should be based on the study of urbanization problems. The complexity of this issue requires improvement of urban building analysis processes, for example, using remote sensing data. To obtain estimates suitable for further analysis and decision-making, a method for studying urban development based on remote sensing data is proposed. It is uses of deep learning in processing structured data using the Image Analyst ArcGIS Pro 3.4. A scheme of the algorithm for obtaining a Deep Learning model for decrypting urban building objects has been developed, and its software implementation has been proposed. The study of the capabilities of the developed method was carried out when solving the problems of updating information about the urban development of Kyiv. The resulting Deep Learning model recognizes urban building objects well and has good adaptability to objects other than the training sample. Conclusions: The choice of the direction of intellectualization should be based on a thorough study of the situation that has arisen as a result of the urbanization. An experimental analysis of the dynamics of changes in the urban development of the Kyiv district for the period 2005 – 2021 showed: an increase in the number of residential (by 5.4%) and commercial development (by 11%), neglect of the development of social infrastructure, non-development of transport infrastructure, ignoring issues of landscaping the territory of the district, etc. The results obtained can be useful in forming priority ways of introducing intelligent solutions into the everyday life of city residents.
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