RADIOELECTRONIC AND COMPUTER SYSTEMS

Ukraine is an associate member of the European Union and in the coming years it is expected that all data and services already used by EU countries will be available to Ukraine. The lack of quality national products for assessing the development and planning of urban growth makes it impossible to assess the impact of cities on the environment and human health. The first steps to create such products for the cities of Ukraine were initiated within the European project "SMart URBan Solutions for air quality, disasters and city growth" (SMURBS), in which specialists from the Space Research Institute of NAS of Ukraine and SSA of Ukraine received the first city atlas for the Kyiv city, which was similar to the European one. However, the resulting product had significantly fewer types of land use than the European one and therefore the question of improving the developed technology arose. The main purpose of the work is to analyze the existing technology of European service Urban Atlas creation and its improvement by developing a unified algorithm for building an urban atlas using all available open geospatial and satellite data for the cities of Ukraine. The development of such technology is based on our own technology for classifying satellite time series with a spatial resolution of 10 meters to build a land cover map, as well as an algorithm for unifying open geospatial data to urban atlases Copernicus. The technology of construction of the city atlas developed in work, based on the intellectual model of classification of a land cover, can be extended to other cities of Ukraine. In the future, the creation of such a product on the basis of data for different years will allow to assess changes in land use and make a forecast for further urban expansion. The proposed information technology for constructing the city atlas will be useful for assessing the dynamics of urban growth and closely related social and economic indicators of their development. Based on it, it is also possible to assess indicators of achieving the goals of sustainable development, such as 11.3.1 "The ratio of land consumption and population growth." The study shows that the city atlas obtained for the Kyiv city has a high level of quality and has comparable land use classes with European products. It indicates that such a product can be used in government decision-making services.

Urban Atlas; Smart City; urban growth; sustainable development goals; indicator 11.3.1; Copernicus

Urban Atlas. Available at: https://land.copernicus.eu/local/urban-atlas (accessed 26.07.2021).

ATLAS OF URBAN EXPANSION. Available at: http://www.atlasofurbanexpansion.org (accessed 26.07.2021).

Ramacher, M., Karl, M., Athanasopolou, E., Kakouri, A., Speyer, O., Matthias, V. A novel approach for dynamic population activity in urban-scale exposure estimates. Air Quality, EGU, General Assembly. Online, 2020, 4–8 May 2020. DOI: 10.5194/egusphere-egu2020-2576.

SMURBS. Available at: https://smurbs.eu/the-project (accessed 26.07.2021).

Shelestov, A., Kolotii, A., Lavreniuk, M., Yailymov, B., Shumilo, L., Korsunska Y. Smart City Services for Kiev City Within ERA-PLANET SMURBS Project. In 2019 IEEE 39th Interna-tional Conference on Electronics and Nanotechnology (ELNANO), 2019, pp. 784-788.

Micek, O., Feranec, J., & Stych, P. Land use/land cover data of the urban atlas and the cadastre of real estate: An evaluation study in the Prague Metropolitan Region. Land, 2020, vol. 9, no. 5, pp. 1-24. DOI: 10.3390/land9050153.

Lavreniuk, M., Kussul, N., Novikov, A. Deep learning crop classification approach based on coding input satellite data into the unified hyperspace. 38th International Conference on Electronics and Nanotechnology (ELNANO), 2018, pp. 239-244. DOI: 10.1109/ELNANO.2018.8477525.

Moskalenko, V., Zaretskyi, M., Korobov, A., Kovalskyi, Y., Shaiekhov, A., Semashko, V., Panyc, A. Model' ta metod navchannya dlya klasyfikatsiynoho analizu rivnya vody v stichnykh trubakh za danymy video inspektsiyi [Model and training method for water level classification in sewer pipes based on video inspection data]. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2021, no. 2(98), pp. 4-15. DOI: 10.32620/reks.2021.2.01.

Kushnir, M., Tokarieva, K. Vykorystannya system shtuchnoho intelektu u zadachakh prohnozuvannya finansovykh indeksiv: ohlyad naukovykh dzherel [Artificial intelligence systems in the financial market predictions: literature review]. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2020, no. 3(95), pp. 108-117. DOI: 10.32620/reks.2020.3.11.

Lavreniuk, M., Novikov, A. Oglyad metodiv mashy`nnogo navchannya dlya klasy`fikaciyi vely`ky`x obsyagiv suputny`kovy`x dany`x [Review of machine learning methods for Big satellite Data classification]. Sy`stemni doslidzhennya ta informacijni texnologiyi – System research and information technologies, 2018, no. 1, pp. 52-71. DOI: 10.20535/SRIT.2308-8893.2018.1.04.

Shelestov, A. Using the fuzzy-ellipsoid method for robust estimation of the state of a grid system node. Cybernetics and Systems Analysis, 2008, vol. 44, no. 6, pp. 847-854. DOI: 10.1007/s10559-008-9057-1.

Global Human Settlement Layer. Available at: https://ghsl.jrc.ec.europa.eu (accessed 26.07.2021).

Kussul, N., Lavreniuk, M., Shumilo, L. Deep Recurrent Neural Network for Crop Classification Task Based on Sentinel-1 and Sentinel-2 Imagery. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 6914-6917. DOI: 10.1109/IGARSS39084.2020.9324699.

Lavreniuk, M., Kussul, N., Novikov, A. Deep learning crop classification approach based on sparse coding of time series of satellite data. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 4812-4815. DOI: 10.1109/IGARSS.2018.8518263.

OpenStreetMap. Available at: https://wiki.openstreetmap.org/wiki/Accuracy (accessed 26.07.2021).

Kussul, N., Lavreniuk, M., Kolotii, A., Skakun, S., Rakoid, O., & Shumilo, L. A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data. International Journal of Digital Earth, 2020, vol. 2, no. 13, pp. 309-321. DOI: 10.1080/17538947.2019.1610807.

Shelestov, A., Kussul, N., Yailymov, B., Shumilo, L., Bilokonska, Y. Assessment of Land Consumption for SDG Indicator 11.3. 1 Using Global and Local Built-Up Area Maps. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 4971-4974. DOI: 10.1109/IGARSS39084.2020.9324390.

Kussul, N., Shelestov, A., Skakun, S., & Kravchenko, O. Data assimilation technique for flood monitoring and prediction. Int. J. on Information Theory and Applications, 2008, vol. 15, no. 1, pp. 76-84.