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Information × Registration Number 0223U004688, 0121U114416 , R & D reports Title Deep learning methods and models for applied problems of satellite monitoring popup.stage_title Head Lavreniuk Mykola S., Доктор філософії Registration Date 29-11-2023 Organization Space Research Institute of National Academy od Science and State Space Agency of Ukraine popup.description2 Purpose of work - improvement of existing and development of new deep learning methods and models to solve problems of monitoring economic indicators and business intelligence based on the fusion of heterogeneous satellite data and data from different sources of high spatial and temporal resolution. Research methods - machine learning methods, statistical analysis, information technologies of big data analysis, improvement of deep learning models, use of recurrent neural networks, method of increasing spatial distinction of satellite data based on deep learning methods, methods of mathematical statistics and geospatial analysis. Using cloud environments for data processing. A method for adaptation recurrent neural networks to integrate and harmonize satellite data. Results: A new method of classifying multidimensional geospatial data based on recurrent neural networks has been developed, which allows to obtain output in the form of a multidimensional time series. The improved architecture of the deep learning model for the classification of time series of geospatial data using recurrent neural networks based on transfer learning, which allows applying the trained neural network to other sets of time series data. A new method of increasing the spatial resolution of satellite data based on deep learning methods using generative adversarial networks has been developed to increase the accuracy of identification and localization of objects, which allows to increase the accuracy of monitoring economic activity based on geospatial data. The developed methods have been used to solve applied problems of satellite monitoring and implemented in AWS and GEE cloud environments. With the use of heterogeneous data, as well as the products of satellite data processing, in particular, the quality of atmospheric air and the night lighting, the economic indicators were analyzed. Product Description popup.authors Yemelianov Mykhailo O. Gordiyko Nataliya O. Zhukovskyi Anton L. Krasilnikova Tetyana M Kussul Nataliia M. Ohrimenko Anton Oleksandrovych Parhomchyk Oleksandr M. Shumilo Leonid L. Yailymov Bohdan Ya. popup.nrat_date 2023-11-29 Close