Перегляд за Автор "Yailymov, Bohdan"
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Документ Відкритий доступ Advanced Method of Land Cover Classification Based on High Spatial Resolution Data and Convolutional Neural Network(Anhalt University of Applied Sciences, 2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Shumilo, Leonid; Lavreniuk, Mykola; Lavreniuk, Alla; Sylantyev, Sergiy; Kussul, NataliiaДокумент Відкритий доступ Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment(Remote Sensing, 2021-11) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Kussul, NataliiaДокумент Відкритий доступ Cloud Platforms and Technologies for Big Satellite Data Processing(Springer, 2023) Kussul, Nataliia; Shelestov, Andrii; Yailymov, BohdanThis paper addresses the problem of processing large volumes of satellite data and compares different cloud platforms for potential solutions. Existing cloud platforms like Google Earth Engine, Amazon Web Services (AWS), and CREODIAS have been used to tackle this challenge. However, this study proposes an optimal pipeline for satellite data processing, taking into account the advantages and limitations of each platform. The specific focus is on solving machine learning problems using satellite data. In the experiment conducted, the effectiveness of each cloud platform was analyzed. It was found that cloud platforms offer benefits such as flexibility, access to computing resources, and parallel processing architectures, leading to increased productivity and cost reduction. CREODIAS, in particular, stands out due to its specialization in satellite data and easy access to various data types, along with tools for data searching and visualization. The experiment demonstrated that tasks, from data loading to classification, were executed fastest on CREODIAS resources. However, AWS performed data classification faster. The availability of its own internal data bucket was a significant advantage of CREODIAS, especially when considering ARD data. These findings contribute to the advancement of AI methodologies and have practical implications for solving satellite monitoring applications.Документ Відкритий доступ Fire Danger Assessment Based on the Improved Fire Weather Index(IEEE, 2022) Kussul, Nataliia; Yailymov, Bohdan; Shelestov, Andrii; Yailymova, HannaДокумент Відкритий доступ Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data(MDPI, 2023) Kussul, Nataliia; Fedorov, Oleh; Yailymov, Bohdan; Pidgorodetska, Liudmyla; Kolos, Liudmyla; Yailymova, Hanna; Shelestov, AndriiFire is one of the most common disturbances in natural ecosystems. The analysis of various sources of information (official and unofficial) about the fires in Ukraine (2019–2020) showed a lack of timely and reliable information. Satellite observation is of crucial importance to provide accurate, reliable, and timely information. This paper aims to modify the index of fire danger of a forest’s FWI by increasing its precision, based on the use of higher spatial resolution satellite data. A modification of the FWI method involves the utilization of the soil moisture deficit, in addition to the six subindices of the FWI system. In order to calculate the subindices values, weather data from the Copernicus Atmosphere Monitoring Service were used. Soil moisture deficit is calculated using Sentinel-1 radar satellite data on the water saturation degree of the soil surface layer and geospatial parameters from the 3D Soil Hydraulic Database of Europe. The application of the proposed methodology using the specified satellite, weather, and geospatial data makes it possible to assess fire danger on a continental scale with a spatial resolution of 250 m, 1 km, and a daily temporal resolution. Validation of the proposed method for modifying the FWI system demonstrates an improvement in the precision and relevance of fire danger prediction.Документ Відкритий доступ Fusion of classification algorithms for landfill detection in Ukraine(2022) Shelestov, Andrii; Yailymov, Bohdan; Yailymova, Hanna; Mikava, PolinaДокумент Відкритий доступ Geospatial Analysis of Leased Lands in Ukraine(IEEE, 2021) Shelestov, Andrii; Yailymov, Bohdan; Parkhomchuk, OleksandrДокумент Відкритий доступ Geospatial Analysis of Life Quality in Ukrainian Rural Areas(IEEE, 2023) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, AndriiIn this work, the authors developed an initial algorithm for assessing the quality of life in rural areas of Ukraine using the aggregation of heterogeneous geospatial information. The approach consists in a comprehensive assessment of the remoteness of the village from vital infrastructure facilities (hospitals, educational institutions, banks, libraries, shops, roads, power lines, etc.), to natural ecosystems (water bodies, forests or parks), as well as to occupied territories. The obtained results show that the largest number of villages with a depressed quality of life are located in the eastern and southern territories of the country, and with a positive quality - mainly in the west and central Ukraine. This, of course, is partly related to active hostilities, but considering that the proposed algorithm works based on the analysis of distances to various objects, it can be concluded that the war only worsened the condition of life in the villages.Документ Відкритий доступ Geospatial monitoring of sustainable and degraded agricultural land(2023-07) Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia; Shelestov, Andrii; Shumilo, LeonidIn this study, the assessment of sustainable development goal (SDG) indicator 2.4.1 for Ukraine and Germany is conducted using geospatial and satellite data. The traditional methodology for the SDG indicator 2.4.1 calculation cannot be directly applied to the Ukrainian territory due to the lack of systematic data collection of the essential indicators. Therefore, the authors have developed an integrated approach to estimate land degradation, that uses different schemes for various land cover and crop types at the national scale, utilizing satellite data and employing the WOFOST model for crop growing simulation. The research describes the information sources used for creation crop type classification maps and the necessary data for modeling leaf area index (LAI) based on the WOFOST model. The calculated indicators are determined for each Ukrainian region from 2018 to 2022. Observations in 2022 show a decline in the indicator 2.4.1 across nearly all regions of Ukraine, directly attributed to the military conflicts within the Ukraine. To assess the possibility of applying the developed technology to a large area, the indicator was calculated for a European country (Germany).Документ Відкритий доступ Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine(MDPI, 2023-10) Yailymov, Bohdan; Shelestov, Andrii; Yailymova, Hanna; Shumilo, LeonidWildfires cause extensive damage, but their rapid detection and cause assessment remains challenging. Existing methods utilize satellite data to map burned areas and meteorological data to model fire risk, but there are no information technologies to determine fire causes. It is crucially important in Ukraine to assess the losses caused by the military actions. This study proposes an integrated methodology and a novel framework integrating burned area mapping from Sentinel-2 data and fire risk modeling using the Fire Potential Index (FPI) in Google Earth Engine. The methodology enables efficient national-scale burned area detection and automated identification of anthropogenic fires in regions with low fire risk. Implemented over Ukraine, 104.229 ha were mapped as burned during July 2022, with fires inconsistently corresponding to high FPI risk, indicating predominantly anthropogenic causes.Документ Відкритий доступ Ground Based Validation of Copernicus Atmosphere Monitoring Service Data for Kyiv(IEEE, 2021) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Samoilenko, Oleg; Shumilo, LeonidДокумент Відкритий доступ Intellectual Analysis of Major Crops Area due to Climate Changes in Ukraine(IEEE, 2021) Emelyanov, Mikhail; Yailymova, Hanna; Shelestov, Andrii; Yailymov, BohdanДокумент Відкритий доступ Losses Assessment for Winter Crops Based on Satellite Data and Fuzzy Logic(IEEE, 2021) Bilokonska, Yuliia; Yailymova, Hanna; Yailymov, Bohdan; Shelestov, Andrii; Shumilo, Leonid; Lavreniuk, MykolaДокумент Відкритий доступ Monitoring of Fires Caused by War in Ukraine Based on Satellite Data(IEEE, 2023) Yailymov, Bohdan; Yailymova, Hanna; Shelestov, Andrii; Shumilo, LeonidThe focus of this paper is on fire monitoring studies which utilize a variety of satellite data. The study examines various data sources that are used to automatically detect fires at a national level in Ukraine. Existing fire monitoring systems have low spatial resolution, which makes it difficult to detect fires. Therefore, the authors propose an approach that uses both low- and high-resolution satellite data for fire monitoring. The study found that MODIS, Landsat-8,9 and Sentinel-2 satellite data provide reliable information for fire monitoring in Ukraine. These data sources offer a variety of benefits, including high spatial resolution, frequent revisit times, and wide spectral coverage. In order to understand how favourable weather conditions are for the occurrence of fire, the authors used the Fire Potential Index (FPI). The methodology developed in this study provides a promising approach to monitoring fires and understanding the causes of fires, particularly those caused by the war in Ukraine. The authors implemented the fire detection methodology and FPI index assessment in the Google Earth Engine cloud platform, which allowed for efficient processing and analysis of large volumes of data. In conclusion, the paper highlights the importance of using a combination of low- and high-resolution satellite data for fire monitoring.Документ Відкритий доступ Neural Network Model for Monitoring of Landfills Using Remote Sensing Data(IEEE, 2022-10-25) Yailymova, Hanna; Mikava, Polina; Kussul, Nataliia; Krasilnikova, Tetiana; Shelestov, Andrii; Yailymov, Bohdan; Titkov, DmytroДокумент Відкритий доступ Sustainable Development Goal 2.4.1 for Ukraine Based on Geospatial Data(2023) Yailymova, Hanna; Yailymov, Bohdan; Mazur, Yevhen; Kussul, Nataliia; Shelestov, AndriiIn this work, the indicator of sustainable development goal (SDG) 2.4.1 for Ukraine is calculated based on geospatial and satellite data. The generally accepted technology for calculating the given indicator cannot be applied for the territory of Ukraine due to the lack of systematic collection of the necessary indicators. Therefore, the authors have developed the complex method for land degradation estimation that uses different schemes for separate land cover and crop types at the country level based on satellite and modeling data using WOFOST model. The paper describes the sources of information used to create crop type classification maps and the data required for leaf area index (LAI) modeling for the WOFOST model. Calculated indicators from 2018 to 2022 for each of the regions of Ukraine. In 2022, the decrease of the indicator is monitored in almost all regions of Ukraine, which is a direct result of military actions on the territory of Ukraine.Документ Відкритий доступ The Wetland Map Validation for Ukraine(IEEE, 2021) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Chyrkov, ArtemДокумент Відкритий доступ Використання супутникових продуктів для аналізу змін територій природно-заповідного фонду України(Проблем и керування та інформатики, 2022) Yailymov, Bohdan; Yailymova, Hanna; Shelestov, Andrii; Lavreniuk, AllaДокумент Відкритий доступ Інтелектуальні методи та моделі обробки супутникових даних у задачі моніторингу звалищ(Проблем и керування та інформатики, 2022) Yailymova, Hanna; Yailymov, Bohdan; Shelestov, Andrii; Krasilnikova, Tetiana