Статті КІТС (ДМетІ)

Permanent URI for this collectionhttp://crust.ust.edu.ua/handle/123456789/14595

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Information System for Abandoned Arable Land Detection From Sentinel-2 Images
(CEUR-WS Team, Aachen, Germany, 2025) Akymenko, Karyna; Sergieieva, Kateryna L.; Kavats, Yurii V.; Kovrov, Oleksandr S.
ENG: An information system for the automated detection of abandoned arable land, based on Sentinel-2 satellite images, is developed. The system provides monitoring of agricultural land, even in areas where ground surveys are challenging to conduct. Integrated with Google Earth Engine (GEE), the system classifies agricultural areas as cultivated or abandoned in near real time based on Normalized Difference Vegetation Index (NDVI) time series. It supports two modes of operation: local analysis of GeoTIFF files and cloud analysis using an interactive map. Its classification method compares the maximum NDVI values for the target and reference years, enabling the detection of the characteristics of the vegetation cover degradation of abandoned land. The results were experimentally validated for a sample of agricultural areas in the Dnipropetrovsk and Donetsk Oblasts. The proposed system can detect abandoned arable land with an accuracy of up to 92.5% (F1-score: 0.898), even in areas of military conflict where ground observations are unavailable.
Machine Learning-Based Monitoring of War-Damaged Water Bodies in Ukraine Using Satellite Images
(CEUR-WS Team, Aachen, Germany, 2024) Sergieieva, Kateryna L. ; Kavats, Olena O.; Vasyliev, Volodymyr; Kavats, Yurii V.; Kovrov, Oleksandr
ENG: Water resources are Ukraine's strategic environmental asset. As a result of the destruction caused by the Russo-Ukrainian War, critical water infrastructure has been severely damaged. This makes it essential to effectively manage and conserve water resources in the face of increasing anthropogenic impact. The use of machine learning methods to monitor water bodies' conditions based on optical and Synthetic Aperture Radar (SAR) satellite images allows for automating analysis processes and providing more accurate and timely results, which is important for making reasonable management decisions. In this study, information tools for mapping and assessing the dynamics of surface water body changes were developed based on Sentinel-1 and Sentinel-2 data using a convolutional neural network. They were used for the mapping of surface water bodies in the Lower Dnipro sub-basin affected by the destruction of the Kakhovka Hydropower Plant dam. To improve the result of satellite image mixed pixels classification in shallow areas of swampy water bodies at the bottom of the destroyed Kakhovka Reservoir, it is proposed to use a block data model and a probabilistic approach to assess the presence of "water" and "ground" class objects in the images, which allows to achieve mapping accuracy of up to 96%.