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https://ela.kpi.ua/handle/123456789/43459

У зібранні розміщено матеріали, що опубліковані або готуються до публікації в наукових журналах та збірниках.

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A generalized model for mapping sunflower areas using Sentinel-1 SAR data
(Elsevier, 2024) Qadir, Abdul; Skakun, Sergii; Kussul, Nataliia; Shelestov, Andrii; Becker-Reshef, Inbal
Existing crop mapping models, rely heavily on reference (calibration) data obtained from remote sensing observations. However, the transferability of such models in space and time, without the need for additional extensive datasets remains a significant challenge. There is still a large gap in developing generalized classification models capable of mapping specific or multiple crops with minimal calibration data. In this study, we present a generalized automatic approach for sunflower mapping at 20-m spatial resolution, using the C-band Sentinel-1 (S1) synthetic aperture radar (SAR) data driven by previously developed phenological metrics. These metrics characterize the directional behavior of the sunflower head, capturing distinct backscattering responses in SAR data acquired from ascending and descending orbits. Specifically, we utilize SAR-derived backscatter values in VH and VV polarization, as well as their ratio VH/VV, as input features to a random forest classifier that was calibrated for the year 2022 in Ukraine. This model is further directly applied to selected sites for multiple years in Ukraine (generalization in time) and other major sunflower producing countries (generalization in space): Ukraine for 2018–2020, and Hungary, France, Russia and USA for 2018. Our results reveal that the model based on features acquired from descending orbits outperforms its ascending orbit counterpart because of the directional behavior of sunflower: user's accuracy (UA) of 96%, producer's accuracy (PA) of 97% and F-score of 97% (descending) compared to UA of 90%, PA of 89% and F-score of 90% (ascending). When generalized to other years and countries, our model achieves an F-score exceeding 77% for all cases, with the highest F-scores (>91%) observed in Ukraine and Russia sites and the lowest (77%) for the US site. We further utilize the produced maps (pixel-based) for the selected regions and years to estimate sunflower planted areas using a statistical sampling-based approach. Our estimates yield the relative root mean square error (RMSE) as 19.7% of the mean area, when compared to the reference data from official statistics and reference maps. These findings demonstrate the robustness of our proposed approach across space and time in generating accurate sunflower maps, its ability to mitigate cloud cover issues through spaceborne SAR data acquisitions, and its potential for obtaining estimates of sunflower planted areas. This research emphasizes the importance of developing interpretable and domain-specific machine learning models that can be readily extended to multiple geographical regions with little to no labelled datasets.
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Acceleration of computations in modelling of processes in complex objects and systems
(Інститут загальної енергетики НАН України, 2024) Khaidurov, Vladyslav; Tatenko, Vadym; Lytovchenko,; Tsiupii, Tamara; Zhovnovach, Tetiana
The development of methods of parallelization of computing processes, which involve the decomposition of the computational domain, is an urgent task in the modeling of complex objects and systems. Complex objects and systems can contain a large number of elements and interactions. Decomposition allows you to break down a system into simpler subsystems, which simplifies the analysis and management of complexity. By dividing the calculation area of the part, it is possible to perform parallel calculations, which increases the efficiency of calculations and reduces simulation time. Domain decomposition makes it easy to scale the model to work with larger or more detailed systems. With the right choice of decomposition methods, the accuracy of the simulation can be improved, since different parts of the system may have different levels of detail and require appropriate methods of additional analysis. Decomposition allows the simulation to be distributed between different participants or devices, which is relevant for distributed systems or collaborative work on a project. In this work, mathematical models are built, which consist in the construction of iterative procedures for "stitching" several areas into a single whole. The models provide for different complexity of calculation domains, which makes it possible to perform different decomposition approaches, in particular, both overlapping and non-overlapping domain decomposition. The obtained mathematical models of subject domain decomposition can be applied to objects and systems that have different geometric complexity. Domain decomposition models that do not use overlap contain different iterative methods of "stitching" on a common boundary depending on the types of boundary conditions (a condition of the first kind is a Dirichlet condition, or a condition of the second year is a Neumann condition), and domain decomposition models with an overlap of two or more areas consist of the minimization problem for constructing the iterative condition of "stitching" areas. It should be noted that the obtained models will work effectively on all applied tasks that describe the dynamic behavior of objects and their systems, but the high degree of efficiency of one model may be lower than the corresponding the degree of effectiveness of another model, since each task is individual.
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Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment
(Remote Sensing, 2021-11) Shelestov, Andrii; Yailymova, Hanna; Yailymov, Bohdan; Kussul, Nataliia
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Assessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning
(2023) Kussul, Nataliia; Drozd, Sofiia; Yailymova, Hanna; Shelestov, Andrii; Lemoine, Guido; Deininger, Klaus
The ongoing full-scale Russian invasion of Ukraine has led to widespread damage of agricultural lands, jeopardizing global food security. Timely detection of impacted fields enables quantification of production losses, guiding recovery policies and monitoring military actions. This study presents a robust methodology to automatically identify agricultural areas damaged by wartime ground activities using free Sentinel-2 satellite data. The 10 m resolution spectral bands and vegetation indices are leveraged, alongside their statistical metrics over time, as inputs to a Random Forest classifier. The algorithm efficiently pinpoints damaged fields, with accuracy metrics around 0.85. Subsequent anomaly detection delineates damages within the fields by combining spectral bands and indices. Applying the methodology over 22 biweekly periods in 2022, approximately 500 thousand ha of cropland across 10 regions of Ukraine were classified as damaged, with the most significant impacts occurring from March to September. The algorithm provides updated damage information despite cloud cover and vegetation shifts. The approach demonstrates the efficacy of automated satellite monitoring to assess agricultural impacts of military actions, supporting recovery analysis and documentation of war crimes.
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Assessment of war-induced agricultural land use changes in Ukraine using machine learning applied to Sentinel satellite data
(Elsevier BV, 2025-05-08) Nataliia Kussul; Andrii Shelestov; Bohdan Yailymov; Hanna Yailymova; Guido Lemoine; Klaus Deininger
The ongoing war in Ukraine has significantly disrupted agricultural land use, leading to reduced cropland areas, increased land abandonment, and heightened uncertainty in food production. This study presents a multi-year assessment of war-induced agricultural land use changes in Ukraine using machine learning-based classification applied to Sentinel-1 and Sentinel-2 satellite imagery. By leveraging cloud computing platforms, including Google Earth Engine (GEE) and the Copernicus Data Space Ecosystem (CDSE), we develop high-resolution KPI-Ukraine (Igor Sikorsky Kyiv Polytechnic Institute (KPI) in Ukraine) land use maps spanning from 2016 to 2024. The study integrates Random Forest and Multi-Layer Perceptron classification techniques to improve accuracy, addressing spectral ambiguities and classification noise. Additionally, a novel transfer learning approach enables reliable classification in conflict-affected areas with limited ground-truth data. We achieved high classification accuracy across the 14 major crop types in Ukraine and abandoned land, validated through F1-scores exceeding 90% for most classes. The fusion of the results generated on the GEE and CDSE platforms enhanced the classification accuracy for minor classes. Our analysis reveals significant reductions in cultivated land in 2022-2024, particularly in conflict zones, where agricultural activity has been heavily disrupted. Overall, Ukraine’s arable land area shrunk by 10% nationwide. The consistently high accuracy of our classification methodology across the nine-year study period demonstrates its robustness and suitability for long-term monitoring of agricultural dynamics in conflict-affected regions and provides a valuable tool for guiding post-war recovery efforts. Our findings underscore the importance of leveraging satellite data for timely and accurate land use monitoring, supporting policymakers in addressing food security challenges and promoting sustainable agricultural practices. This framework also holds potential for broader applications in monitoring land use changes in conflict zones and regions undergoing rapid environmental shifts.
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Automated detection and assessment of war-induced damage to agricultural fields using satellite imagery
(Одеський національний технологічний університет, 2024) Kussul, N.; Drozd, S.; Yailymova, H.
This paper introduces a methodology based on machine learning and remote sensing for detecting military-induced damages to agricultural lands in Ukraine using free Sentinel-2 satellite data. The most informative spectral bands (B2, B3) and vegetation indices (NDVI, GCI) were experimentallyselected for recognizing damaged fields through the Random Forest classification algorithm. Additionally, an anomaly detection method based on the estimation of deviations of pixel values from the mean within each field was applied to determine local damage in the identified affected fields. The proposed methodology demonstrated high classification accuracy with an f1-score of 0.87%, producer’s accuracy of 0.89%, user’s accuracy of 0.85, and sensitivity for detecting local damage. The developed anomaly detection method allows to recognize damage visible on the 10-meter pixel of the Sentinel-2 satellite, but does not identify small craters.Cloudiness of satellite images can significantly impair the accuracy of damage detection, and the method of local damage detection can respondto non-military anomaliesand requires careful selection of threshold coefficients for each field. The study conducted a comprehensive assessment of damages inflicted on Ukrainian agricultural fields during the period 2022-2023, revealing that a total of 1,544,952 hectares, equivalent to 5.72% of the total agricultural area, experienced damage. This included 509,107 ha of wheat, 114,302 ha of sunflower, 68,830 ha of maize, 4,029 ha of rapeseed, and 16,561 ha of other crops. The most affected regions were Donetsk, Zaporizhia, and Kherson oblasts. The comprehensive findings of this research provide valuable insights for monitoring the state of agriculture and formulating strategic plans for the recovery of agricultural resources amidst the ongoing military conflict.
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Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields
(Sustainability, 2022) Kussul, Nataliia; Deininger, Klaus; Shumilo, Leonid; Lavreniuk, Mykola; Ayalew Ali, Daniel; Nivievskyi, Oleg
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Comparative analysis of classification techniques for topic-based biomedical literature categorisation
(Frontiers Media S.A., 2023) Stepanov, Ihor; Ivasiuk, Arsentii; Yavorskyi, Oleksandr; Frolova, Alina
Introduction: Scientific articles serve as vital sources of biomedical information, but with the yearly growth in publication volume, processing such vast amounts of information has become increasingly challenging. This difficulty is particularly pronounced when it requires the expertise of highly qualified professionals. Our research focused on the domain-specific articles classification to determine whether they contain information about drug-induced liver injury (DILI). DILI is a clinically significant condition and one of the reasons for drug registration failures. The rapid and accurate identification of drugs that may cause such conditions can prevent side effects in millions of patients. Methods: Developing a text classification method can help regulators, such as the FDA, much faster at a massive scale identify facts of potential DILI of concrete drugs. In our study, we compared several text classification methodologies, including transformers, LSTMs, information theory, and statistics-based methods. We devised a simple and interpretable text classification method that is as fast as Naïve Bayes while delivering superior performance for topic-oriented text categorisation. Moreover, we revisited techniques and methodologies to handle the imbalance of the data. Results: Transformers achieve the best results in cases if the distribution of classes and semantics of test data matches the training set. But in cases of imbalanced data, simple statistical-information theory-based models can surpass complex transformers, bringing more interpretable results that are so important for the biomedical domain. As our results show, neural networks can achieve better results if they are pre-trained on domain-specific data, and the loss function was designed to reflect the class distribution. Discussion: Overall, transformers are powerful architecture, however, in certain cases, such as topic classification, its usage can be redundant and simple statistical approaches can achieve compatible results while being much faster and explainable. However, we see potential in combining results from both worlds. Development of new neural network architectures, loss functions and training procedures that bring stability to unbalanced data is a promising topic of development.
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Crop Yield Forecasting for Major Crops in Ukraine
(IEEE, 2021) Shelestov, Andrii; Shumilo, Leonid; Yailymova, Hanna; Drozd, Sophia
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Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery
(2025) Lavreniuk, Mykola; Kussul, Nataliia; Shelestov, Andrii; Yailymov, Bohdan; Salii, Yevhenii; Kuzin, Volodymyr; Szantoi, Zoltan
The accurate delineation of agricultural field boundaries from satellite imagery is vital for land management and crop monitoring. However, current methods face challenges due to limited dataset sizes, resolution discrepancies, and diverse environmental conditions. We address this by reformulating the task as instance segmentation and introducing the Field Boundary Instance Segmentation - 22M dataset (FBIS-22M), a large-scale, multi-resolution dataset comprising 672,909 high-resolution satellite image patches (ranging from 0.25 m to 10 m) and 22,926,427 instance masks of individual fields, significantly narrowing the gap between agricultural datasets and those in other computer vision domains. We further propose Delineate Anything, an instance segmentation model trained on our new FBIS-22M dataset. Our proposed model sets a new state-of-the-art, achieving a substantial improvement of 88.5% in mAP@0.5 and 103% in mAP@0.5:0.95 over existing methods, while also demonstrating significantly faster inference and strong zero-shot generalization across diverse image resolutions and unseen geographic regions. Code, pre-trained models, and the FBIS-22M dataset are available at https://lavreniuk.github.io/Delineate-Anything.
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Discrete Atomic Transform-Based Lossy Compression of Three-Channel Remote Sensing Images with Quality Control
(Remote Sensing, 2021) Makarichev, Victor; Vasilyeva, Irina; Lukin, Vladimir; Vozel, Benoit; Shelestov, Andrii; Kussul, Nataliia
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Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data
(MDPI, 2023) Kussul, Nataliia; Fedorov, Oleh; Yailymov, Bohdan; Pidgorodetska, Liudmyla; Kolos, Liudmyla; Yailymova, Hanna; Shelestov, Andrii
Fire 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.
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Flooded and Irrigation Area Monitoring After the Kakhovka Dam Disaster Based on Machine Learning and Satellite Data
(IEEE, 2025) Yailymov, Bohdan; Yailymova, Hanna; Kolotii, Andrii; Shelestov, Andrii; Skakun, Sergii; Baber, Sheila; Becker-Reshef, Inbal; Kussul, Nataliia
This study aims to assess the impact of the Kakhovka Dam destruction in Ukraine that occurred on 6 June 2023, on cropland irrigation using satellite remote sensing data. The main goal of this study is to assess flooded areas and the impact on irrigated area before and after Kakhovka Dam destruction. In particular, we analyzed flooded areas in 2023, and the changes in irrigated areas before and after the dam destruction (in 2019 and 2024) were also assessed. Maps of water bodies were generated before and after the flood using Sentinel-1, Sentinel-2, and Landsat-9 images. The random forest classifier was used for flooded area mapping, while the multilayer perceptron and the U-shaped network classifier were used for irrigated land identification. The main findings are as follows. 1) As of 9 June 2023, the total area of flooding under the Kakhovka Dam was 47.33 thousand hectares (th. ha) (473 km2), affecting 1.67 th. ha of cropland, 0.97 th. ha of forests, 12.3 th. ha of grasslands, 1.85 th. ha of settlements, and 29.4 th. ha of wetlands. 2) The analysis of irrigated area shows a decrease in irrigated cropland—from 351 th. ha in 2019 to 38 th. ha. in 2024. 3) The classification accuracy for 2019 irrigation mapping achieved 90.4% overall accuracy with F1-scores of 90.4% for both irrigated and nonirrigated classes based on ground truth data. 4) The complete disappearance of water in irrigation canals was documented, indicating the systematic destruction of agricultural infrastructure with far-reaching consequences for regional food security. The flood also affected areas along the Ingulets River, which led to the flooding of agricultural land located near the river banks and affected water quality. The disappearance of water in canals used to irrigate cropland is also analyzed, indicating the disruption of irrigation systems and possible far-reaching consequences for agriculture. Thus, this study shows the utility of satellite remote sensing and machine learning approaches for rapid monitoring and quantification of flood-related natural disaster impacts and the analysis of irrigated areas in conflict-affected regions.
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Generative adversarial network augmentation for solving the training data imbalance problem in crop classification
(2023) Shumilo, Leonid; Okhrimenko, Anton; Kussul, Nataliia; Drozd, Sofiia; Shkalikov, Oleh
Deep learning models offer great potential for advancing land monitoring using satellite data. However, they face challenges due to imbalanced real-world data distributions of land cover and crop types, hindering scalability and transferability. This letter presents a novel data augmentation method employing Generative Adversarial Neural Networks (GANs) with pixel-to-pixel transformation (pix2pix). This approach generates realistic synthetic satellite images with artificial ground truth masks, even for rare crop class distributions. It enables the creation of additional minority class samples, enhancing control over training data balance and outperforming traditional augmentation methods. Implementing this method improved the overall map accuracy (OA) and intersection over union (IoU) by 1.5% and 2.1%, while average crop type classes’ user accuracy (UA) and producer accuracies (PA), as well as IoU, were improved by 11.2%, 6.4% and 10.2%.
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Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine
(MDPI, 2023-10) Yailymov, Bohdan; Shelestov, Andrii; Yailymova, Hanna; Shumilo, Leonid
Wildfires 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.
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Improving spatial resolution of chlorophyll-a in the Mediterranean sea based on machine learning
(ХАІ, 2024) Yailymov, Bohdan; Kussul, Nataliia; Henitsoi, Pavlo; Shelestov, Andrii
The objective of this study is to increase the spatial resolution of data on the level of chlorophyll-a in the Mediterranean Sea using satellite images and ground measurements. The goal of this study is to develop an information technology based on machine learning to create chlorophyll-a concentration maps with high spatial resolution for the pilot areas of the Mediterranean Sea. Traditional ground-based methods for measuring chlorophyll-a are time-consuming, expensive, and have limited spatial and temporal coverage. Therefore, satellite observations have become an effective tool for monitoring chlorophyll-a over large areas. Low spatial resolution satellite data such as GCOM-C/SGLI and Sentinel-3 OLCI allow measurements of chlorophyll-a concentration at the sea surface. However, these data have limited accuracy and spatial resolution, which creates challenges for monitoring local changes in coastal zones and small water areas. Tasks: to analyze available satellite data and ground-based measurements of chlorophyll-a for the Mediterranean Sea; to investigate the correlation between satellite data of different spatial resolutions and ground measurements; to select informative features from satellite data for building machine learning models; and to develop models for increasing the spatial resolution of chlorophyll-a based on regression and machine learning algorithms. Obtained results: information technology combining satellite data with ground measurements in the Google Earth Engine cloud platform is proposed; correlations between satellite measurements of chlorophyll-a and ground data are investigated; models based on Random Forest and Multilayer Perceptron with coefficients of determination up to 0.36 and correlation of 0.6 with test data are built; chlorophyll-a maps with a spatial resolution of 10 m are created for the pilot area near Cyprus. Conclusions. The developed information technology allows the effective combination of satellite data of different spatial resolutions and ground measurements to increase the accuracy and detail of chlorophyll-a maps in the Mediterranean Sea. Further research involves improving the preprocessing of satellite data, using more features, involving data from other regions, and applying more sophisticated machine learning models.
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Innovative approaches for forest monitoring using remote sensing and cloud computing
(Akademperiodyka, 2024) Kussul, N.; Shelestov, A.; Yailymov, B.; Yailymova, H.; Lavreniuk, M.; Shumilo, L.; Skakun, S.; Kuzin, V.; Salii, E.; Kolotii, A.
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Innovative models and applications of satellite intelligence
(Akademperiodyka, 2024) Kussul, N.; Shelestov, A.; Drozd, S.; Yailymov, B.; Yailymova, H.; Lavreniuk, M.; Shumilo, L.; Skakun, S.
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Is Soil Bonitet an Adequate Indicator for Agricultural Land Appraisal in Ukraine?
(Sustainability, 2021) Shumilo, Leonid; Lavreniuk, Mykola; Skakun, Sergii; Kussul, Nataliia
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Methods And Algorithms Of Swarm Intelligence For The Problems Of Nonlinear Regression Analysis And Optimization Of Complex Processes, Objects, And Systems: Review And Modification Of Methods And Algorithms
(Інститут загальної енергетики НАН України, 2024-07) Khaidurov, Vladyslav; Tatenko, Vadym; Lytovchenko, Mykyta; Tsiupii, Tamara; Zhovnovach, Tetiana
The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since most serious problems involve the search for tens, hundreds, or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes, and urban systems, reducing energy losses, and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively applied to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, and clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic.

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