Статті КІТС (ДМетІ)

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Aircraft Detection in Aerial Imagery Based on YOLO Architectures
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Radionov, Yevhen; Hnatushenko, Volodymyr V.
ENG: The study is devoted to determining the most efficient YOLO-based architecture for the task of aircraft detection in high-resolution aerial imagery. A comparative analysis was conducted across YOLO models v8 through v11 under three experimental conditions: using pre-trained (raw) models, fine-tuning the models on a domain-specific dataset, and fine-tuning models to a dataset enhanced through a proposed image preprocessing method. The evaluation considered both accuracy and inference performance metrics. The proposed methodology reduced the false negative rate from 19.5% to 3.2% at a confidence threshold of 0.75, underscoring its effectiveness in enhancing target visibility under challenging imaging conditions such as low contrast or background clutter.
Aircraft Detection with Deep Neural Networks and Contour-Based Methods
(National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2024) Radionov, Y. D.; Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Kazymyrenko, O. V.
ENG: Context. Aircraft detection is an essential task in the military, as fast and accurate aircraft identification allows for timely response to potential threats, effective airspace control, and national security. The use of deep neural networks improves the accuracy of aircraft recognition, which is essential for modern defense and airspace monitoring needs. Objective. The work aims to improve the accuracy of aircraft recognition in high-resolution optical satellite imagery by using deep neural networks and a method of sequential boundary traversal to detect object contours. Method. A method for improving the accuracy of aircraft detection on high-resolution satellite images is proposed. The first stage involves collecting data from the HRPlanesv2 dataset containing high-precision satellite images with aircraft annotations. The second stage consists of preprocessing the images using a sequential boundary detection method to detect object contours. In the third stage, training data is created by integrating the obtained contours with the original HRPlanesv2 images. In the fourth stage, the YOLOv8m object detection model is trained separately on the original HRPlanesv2 dataset and the dataset with the applied preprocessing, which allows the evaluation of the impact of additional processed features on the model performance. Results. Software that implements the proposed method was developed. Testing was conducted on the primary data before preprocessing and the data after its application. The results confirmed the superiority of the proposed method over classical approaches, providing higher aircraft recognition accuracy. The mAP50 index reached 0.994, and the mAP50-95 index reached 0.864, 1% and 4.8% higher than the standard approach. Conclusions. The experiments confirm the effectiveness of the proposed method of aircraft detection using deep neural networks and the process of sequential boundary traversal to detect object contours. The results indicate this approach’s high accuracy and efficiency, which allows us to recommend it for use in research related to aircraft recognition in high-resolution images. Further research could focus on improving image preprocessing methods and developing object recognition technologies in machine learning.
Application of Two-Dimensional Padé-Type Approximations for Image Processing
(National University «Zaporizhzhia Polytechnic», Zaporizhzhia, 2023) Olevskyi, V. I.; Hnatushenko, Volodymyr V.; Korotenko, G. M.; Olevska, Yu. B.; Obydennyi, Ye. O.
ENG: Context. The Gibbs phenomenon introduces significant distortions for most popular 2D graphics standards because they use a finite sum of harmonics when image processing by expansion of the signal into a two-dimensional Fourier series is used in order to reduce the size of the graphical file. Thus, the reduction of this phenomenon is a very important problem. Objective. The aim of the current work is the application of two-dimensional Padé-type approximations with the aim of elimination of the Gibbs phenomenon in image processing and reduction of the size of the resulting image file. Method. We use the two-dimensional Padé-type approximants method which we have developed earlier to reduce the Gibbs phenomenon for the harmonic two-dimensional Fourier series. A definition of a Padé-type functional is proposed. For this purpose, we use the generalized two-dimensional Padé approximation proposed by Chisholm when the range of the frequency values on the integer grid is selected according to the Vavilov method. The proposed scheme makes it possible to determine a set of series coefficients necessary and sufficient for construction of a Padé-type approximation with a given structure of the numerator and denominator. We consider some examples of Padé approximants application to simple discontinuous template functions for both formulaic and discrete representation. Results. The study gives us an opportunity to make some conclusions about practical usage of the Padé-type approximation and about its advantages. They demonstrate effective elimination of distortions inherent to Gibbs phenomena for the Padé-type approximant. It is well seen that Padé-type approximant is significantly more visually appropriate than Fourier one. Application of the Padé-type approximation also leads to sufficient decrease of approximants’ parameter number without the loss of precision. Conclusions. The applicability of the technique and the possibility of its application to improve the accuracy of calculations are demonstrated. The study gives us an opportunity to make conclusions about the advantages of the Padé-type approximation practical usage.
Automated Building Damage Detection on Digital Imagery Using Machine Learning
(Dnipro University of Technology, Ukraine, 2023) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Purpose. To develop an automated method based on machine learning for accurate detection of features of a damaged building on digital imagery. Methodology. This article presents an approach that employs a combination of unsupervised machine learning techniques, specifically Principal Component Analysis (PCA), K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), to identify building damage resulting from military conflicts. The PCA method is utilized to identify principal vectors representing the directions of maximum variance in the data. Subsequently, the K-means method is applied to cluster the feature vector space, with the predefined number of clusters reflecting the number of principal vectors. Each cluster represents a group of similar blocks of image differences, which helps to identify significant features associated with fractures. Finally, the DBSCAN method is employed to identify areas where points with similar characteristics are located. Subsequently, a binary fracture mask is generated, with pixels exceeding the threshold being identified as fractures. Findings. The introduced methodology attains an accuracy rate of 98.13 %, surpassing the performance of conventional methods such as DBSCAN, PCA, and K-means. Furthermore, the method exhibits a recall of 82.38 %, signifying its ability to effectively detect a substantial proportion of positive examples. Precision of 58.54 % underscores the methodology’s capability to minimize false positives. The F1 Score of 70.90 % demonstrates a well-balanced performance between precision and recall. Originality. DBSCAN, PCA and K-means methods have been further developed in the context of automated detection of building destruction in aerospace images. This allows us to significantly increase the accuracy and efficiency of monitoring territories, including those affected by the consequences of military aggression. Practical value. The results obtained can be used to improve automated monitoring systems for urban development and can also serve as the basis for the development of effective strategies for the restoration and reconstruction of damaged infrastructure.
Comparative Analysis of Classification Methods for High-Resolution Optical Satellite Images
(Khmelnytskyi National University, Khmelnytskyi, 2024) Hnatushenko, Volodymyr V.; Kashtan, Vita; Chumychov, Denys; Nikulin, Serhii
ENG: High-resolution satellite image classification is used in various applications, such as urban planning, environmental monitoring, disaster management, and agricultural assessment. Traditional classification methods are ineffective due to the complex characteristics of high-resolution multichannel images: the presence of shadows, complex textures, and overlapping objects. This necessitates selecting an efficient classification method for further thematic data analysis. In this study, a comprehensive assessment of the accuracy of the most well-known classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum similarity, spectral angle map, spectral information difference, binary coding, neural network, decision tree, random forest, support vector machine, K-nearest neighbour, and spectral correlation map) is performed. This study comprehensively evaluates various classification algorithms applied to high-resolution satellite imagery, focusing on their accuracy and suitability for different use cases. To ensure the robustness of the evaluation, high-quality WorldView-3 satellite imagery, known for its exceptional spatial and spectral resolution, was utilized as the dataset. To assess the performance of these methods, error matrices were generated for each algorithm, providing detailed insights into their classification accuracy. The average values along the main diagonal of these matrices, representing the proportion of correctly classified pixels, served as a key metric for evaluating overall effectiveness. Results indicate that advanced machine learning approaches, such as neural networks and support vector machines, consistently outperform traditional techniques, achieving superior accuracy across various classes. Despite their high average accuracy, a deeper analysis revealed that only some algorithms are universally optimal. For instance, some methods, such as random forests or spectral angle mappers, exhibited strength in classifying specific features like vegetation or urban structures but performed less effectively for others. This underscores the importance of tailoring algorithm selection to the specific objectives of individual classification tasks and the unique characteristics of the target datasets. This study can be used to select the most effective method of classifying the earth's surface, depending on the tasks of further thematic analysis of high-resolution satellite imagery. Furthermore, it highlights the potential of integrating machine learning-based approaches to enhance the accuracy and reliability of classification outcomes, ultimately contributing to more practical applications.
Computer Modeling of Territory Flooding in the Event of an Emergency at Seredniodniprovska Hydroelectric Power Plant
(Dnipro University of Technology, Ukraine, 2022) Ivanov, D. V.; Hnatushenko, Volodymyr V.; Kashtan, Vita Yu.; Garkusha, I. M.
ENG: Purpose. Computer modeling of territory flooding in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant (HPP). Methodology. The computer model of possible territory flooding at Seredniodniprovska HPP is developed using simulation modeling methods and geometric and hydrological approaches and considers initial boundary conditions of the water-engineering system. Calculations of the wave break height and the half-divided cross-sectional area of the river bed were made and a three-dimensional model of the territory flooding was built using the Python language and ArcGIS Desktop software. Findings. The data for each creation of the hydraulic node, namely the depth and width of the flooded territory, were calculated. This allowed analyzing the macro level considering the triangulation model of the surface. The wave break parameters and flaps (intersections) were taken into account in case of a dam break at a hydroelectric power plant or a rise in the water level. A mathematical model, and a 3D model were developed, and a forecast of the flood zone due to an emergency was made using satellite survey data. Originality. The mathematical method received further development for calculating flood territories in the event of an emergency at Seredniodniprovska Hydroelectric Power Plant, taking into account the parameters of the breakthrough wave and the calculation of cross-sections for the cases when a hydroelectric dam breaks or the water level rises; the method uses one-dimensional and two-dimensional systems of Saint-Venant equations, and geometric and hydrological approaches. A three-dimensional model of the territory flooding is developed to predict possible consequences. Practical value. The obtained results can be used to model the flooding of the territory located near dangerous hydro-technical objects, such as dams, dikes as well as to forecast flooded territories during the construction of drainage and protective structures.
Decentralized Information System for Supply Chain Management Using Blockchain
(RWTH Aachen, Germany, 2022) Sytnyk, Roman; Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.
ENG: Development of international and domestic trade, globalization, creation of longer and more complex supply chains, increase in sales of goods and similar trends lead to an increase in requirements and load on information systems that manage and monitor the shipments of goods, resources and products. The aim of this paper is to make improvements to the existing approaches of building and designing logistics information systems. The paper proposes usage of blockchain technology in order to simplify and make more transparent the processes of monitoring and managing the movement of products between different equal participants in logistics supply chain information systems. A prototype of the supply chain information system based on the use of blockchain technology and smart-contracts using a decentralized Ethereum virtual machine was developed and studied in comparison with traditional approaches.
Deep Learning-Based Segmentation of Multi-Temporal Satellite Imagery for Flood Detection
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: A deep learning-based pixel-based flood zone segmentation approach is proposed using multi-temporal satellite images and topographic and hydrological information. It is proposed to combine heterogeneous data (satellite images before and after the flood, digital elevation model, and hydrographic characteristics) into a single input tensor, allowing the neural network to consider the area's spatial and temporal dynamics and morphometric features. The architecture of the model ensures the preservation of the spatial detail of the flooded area through skip-connection mechanisms, which contributes to the correct identification of flood boundaries. Comparative analysis with FCNN, DeepLabv3, and BASNet confirmed the superiority of the proposed approach (F1-score 82%, Dice 82% for the category 'flooded areas'), which indicates its effectiveness for accurately detecting flooded areas.
Detection of Forest Fire Consequences on Satellite Images Using a Neural Network
(German Society for Photogrammetry, Remote Sensing and Geoinformation, 2023) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Kashtan, Vita; Heipke, Christian
ENG: The objective of this research is the detection of burnt forest areas from Sentinel-2 imagery. The proposed algorithm uses an approach based on convolutional neural networks (CNN). The functionality of the created system allows solving the task, starting from the moment of receiving the input data, image preprocessing and ending with the export of a hot-spot fire polygonal file describing the area that was burnt. These results are compared to methods based on the dNBR and a variant of BAIS2 called dBAIS2, which are generated from measurements in the near and middle IR channels of the Sentinel images. The proposed algorithm was tested on Sentinel satellite images acquired from June to September 2021for the Tizi Ouzou region, Algeria. We found it to have an overall accuracy of 97%, outperforming the results obtained from dNBR and dBAIS2 by large margins.
Enhancing the Quality of CNN-Based Burned Area Detection in Satellite Imagery through Data Augmentation
(Copernicus GmbH (Copernicus Publications) on behalf of the International Society of Photogrammetry and Remote Sensing, 2023) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Soldatenko, Dmytro V.; Heipke, Christian
ENG: This study aims to enhance the quality of detecting burned areas in satellite imagery using deep learning by optimizing the training dataset volume through the application of various augmentation methods. The study analyzes the impact of image flipping, rotation, and noise addition on the overall accuracy for different classes of burned areas in a forest: fire, burned, smoke and background. Results demonstrate that while single augmentation techniques such as flipping and rotation alone did not result in significant improvements, a combined approach and the addition of noise resulted in an enhancement of the classification accuracy. Moreover, the study shows that augmenting the dataset through the use of multiple augmentation methods concurrently, resulting in a fivefold increase in input data, also enhanced the recognition accuracy. The study also highlights the need for further research in developing more efficient CNN models and in experimenting with additional augmentation methods to improve the accuracy of burned area detection, which would benefit environmental protection and emergency response services.
Entropy-Cryptographic Approach for Transmission of Satellite Data in Telecommunication Networks
(Khmelhytskyi National University, Khmelhytskyi, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Amid rapid advances in satellite observations and increasing risks associated with high-resolution multispectral data downloads, stronger satellite image encryption techniques are becoming increasingly important. To address these challenges, the paper proposes a new cryptographic methodology based on entropy and nonlinear methods. This approach offers high resistance against brute-force attacks, generates a high-entropy key, and employs a dynamic non–linear transformation in multi-layered encryption, which is AES-256-bit encrypted. This comprehensive approach is designed to safeguard the security of satellite data in transit from modern cryptanalysis as it traverses communication networks. The approach begins with entropy-based initializations, iteratively expands the key space, and applies adaptive transformations to render the encrypted data highly unpredictable. Experiments with satellite images of various resolutions demonstrate that this approach is resistant to cryptanalysis. The evaluation included measuring entropy, analysing the correlation between neighbouring pixels, and testing resistance to statistical and frequency-based attacks. The encrypted images achieved entropy values close to the theoretical maximum and showed almost no correlation between adjacent pixels, demonstrating the strength and uniformity of the encryption process. Performance tests on systems with multiple threads and processors revealed clear links between execution time, data size, and the level of parallelization. Moderate parallelism provided the best speed improvements, and the method remained scalable for large datasets, making it suitable for high-throughput environments. This approach ensures strong satellite-image robustness in image size, content, or spectral characteristics. The flexible structure and good performance make it a promising candidate for future telecom networks and secure satellite data distribution systems.
Flood Forecasting with Sentinel-2 Images Using Machine Learning
(ISPRS, Hannover, Germany, 2025) Hnatushenko, Viktoriia V.; Kashtan, Vita Y.; Hnatushenko, Volodymyr V.; Heipke, Christian
ENG: This paper proposes a methodology for detecting flooded areas using Sentinel-2 images, followed by flood forecasting based on a combination of the deep neural network U-Net and a support vector machine (SVM). The U-Net architecture classifies a given Sentinel image into the two classes “water” and “no water”, the SVM subsequently performs a near-future prediction of flooded areas based on the U-Net results and additional information (DEM, land use information, precipitation data etc.). Experimental results demonstrate that for a test site in Ukraine the U-Net/SVM model achieves the highest overall accuracy (98.8%), slightly outperforming other models, including Random Forest and SVM. The resulting flood maps provide valuable information for planning rescue operations and territory management, allowing for rapidly identifying areas of flooding. It can thus contribute to a significant reduction in economic losses and an increase in emergency preparedness.
Fractal Analysis of Satellite Night-Time Imagery for Environmental and Urban Monitoring
(ISPRS, Hannover, Germany, 2025) Hnatushenko, Viktoriia V.; Zhurba, Anna O.; Hnatushenko, Volodymyr V.
ENG: Satellite remote sensing technologies offer a powerful means for acquiring high-resolution imagery that captures both natural and anthropogenic features across large geographic regions. One particularly valuable application of satellite data is the analysis of night-time lights, which reflect human activity, urban infrastructure and energy consumption. Traditional image processing methods often struggle to describe the irregular and complex geometries inherent to urban environments and natural landscapes. This research proposed the application of fractal analysis as a methodological tool for characterizing such complexity in night-time satellite images. By applying this to satellite images of Ukraine and the city of Kyiv before and after major attacks on energy infrastructure, it demonstrated how reductions in night-time illumination correspond with significant decreases in fractal dimension. Additionally, the method was applied to assess urban expansion in Dubai and Dnipro across multi-decadal periods. Results show a correlation between increased urbanization and changes in fractal characteristics, providing evidence that fractal metrics can serve as proxies for urban morphology and development. Fractal analysis as an effective method for environmental monitoring, crisis assessment, and urban planning opens pathways for integrating fractal parameters into larger geospatial intelligence systems and provides a foundation for further research on scale-invariant properties of urban and ecological patterns in satellite imagery.
Homomorphic Filtering in Digital Multichannel Image Processing
(Dnipro University of Technology, Dnipro, Ukraine, 2023) Hnatushenko, Volodymyr V.; Spirintseva, O. V.; Spirintsev, V. V.; Kravets, O. V.; Spirintsev, Dmytro V.
ENG: Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.
A Hybrid Neural Network Architecture for Semantic-Contextual Analysis of Emotions in Social Media
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Ovcharenko, Maksym; Ivanko, Artem
ENG: The paper proposes a hybrid neural network architecture for multi-class classification of the emotional state of social media texts, which combines contextual vector representations generated by the BERT model with extended structured features related to the user and message metadata. The proposed approach uses a multilayer perceptron network that combines linguistic and contextual information in a single representation. The results of the experimental study confirm the superiority of the proposed approach over traditional LSTM and CNN architectures, as well as over the separate use of BERT embeddings. The achieved classification accuracy is 90%, and the F1-measure is 0.91, which indicates the high efficiency of the model in conditions of high variability of language structures and stylistic features of social content.
Hybrid Quantum CNN-Based Information Technology for Building Semantic Segmentation in Aerial Imagery
(CEUR-WS Team, Aachen, Germany, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Babets, Dmytro; Cyran, Krzysztof; Wereszczyński, Kamil
ENG: This paper presents information technology for semantic segmentation of building objects in high-resolution aerospace images using a hybrid quantum convolutional neural network (QCNN). Quantitative and visual evaluations demonstrate the effectiveness of the proposed approach compared to classical segmentation models such as U-Net, CNN, and FCN. The hybrid QCNN model achieved high pixel classification accuracy, with a mean accuracy of 0.98 on the training set and 0.97 on the validation set, indicating robust object recognition. Loss values reached minimal levels (0.05 training, 0.07 validation), confirming efficient training without overfitting. Intersection over Union (IoU) scores were 0.90 and 0.89 for training and validation sets, respectively, demonstrating precise building contour delineation. Testing on diverse urban scenes yielded 100% detection accuracy without false positives or negatives. Training dynamics showed rapid convergence, with mean average precision (mAP) increasing to 0.35–0.45 and mAP@50:95 reaching 0.20–0.30, stabilizing after 50 epochs. The proposed technology effectively segments buildings under varying density, geometric complexity, shadows, and background noise. The hybrid QCNN approach enhances segmentation quality and generalization to new images. The results confirm the practical applicability of the developed technology for geospatial monitoring, urban planning, and mapping based on aerospace imagery.
Identification of Objects on Satellite Images Using the Image Texture Properties
(CEUR-WS Team, Aachen, Germany, 2023) Hnatushenko, Volodymyr V.; Shedlovska, Yana; Shedlovsky, Igor; Gorev, Vyacheslav
ENG: This paper focuses on identifying objects in satellite images using image texture properties, which is an important problem in agriculture. Texture segmentation can distinguish areas that correspond to tree plantations. Orchards and tree plantations can cover vast areas with thousands of trees, making the automation of harvest estimation crucial. Satellite images enable the creation of an effective automatic system for counting trees in plantations. In this work, we applied image texture segmentation to identify areas corresponding to agricultural plantations. We calculated textural properties of the image using the gray-level cooccurrence matrix, including mean value, variance, homogeneity, second angular moment, correlation, contrast, divergence, and entropy. These characteristics were used for segmentation, with multi-scale segmentation employed to distinguish areas of the image with specific textures. We proposed an algorithm for counting objects in satellite images, based on identifying individual objects that create a texture according to their spectral characteristics. The images used in this work primarily featured three object classes: trees, soil, and tree shadows. Since trees in gardens and plantations are arranged uniformly and have the same size, they can be easily distinguished from other image pixels based on their spectral characteristics. We analyzed NDVI and NSVDI spectral indices for tree detection and used the automatic spectral index histogram splitting method to distinguish objects with a high index value corresponding to trees.
Improvement of the Algorithm for Setting the Characteristics of Interpolation Monotone Curve
(Lublin University of Technology, Lublin, 2023) Kholodniak, Yuliia; Havrylenko, Yevhen; Halko, Serhii; Hnatushenko, Volodymyr V.; Suprun, Olena; Volina, Tatiana; Miroshnyk, Oleksandr; Shchur, Taras
ENG: Interpolation of a point series is a necessary step in solving such problems as building graphs de-scribing phenomena or processes, as well as modelling based on a set of reference points of the line frames defining the surface. To obtain an adequate model, the following conditions are imposed upon the interpolating curve: a minimum number of singular points (kinking points, inflection points or points of extreme curvature) and a regular curvature change along the curve. The aim of the work is to develop the algorithm for assigning characteristics (position of normals and curvature value) to the interpolating curve at reference points, at which the curve complies with the specified conditions. The characteristics of the curve are assigned within the area of their possible location. The possibilities of the proposed algorithm are investigated by interpolating the point series assigned to the branches of the parabola. In solving the test example, deviations of the normals and curvature radii from the corresponding characteristics of the original curve have been determined. The values obtained confirm the correctness of the solutions proposed in the paper.
Information System for Air Quality Assessment and Data Processing: Design and Implementation
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Volodymyr V.; Bulana, Tetiana; Gomilko, Igor; Molodets, Bohdan; Boldyriev, Daniil
ENG: In recent years, the issue of air quality has become increasingly critical. This article presents a hardware and software-based solution for collecting and processing data from ground stations monitoring air quality. The system is designed to collect real-time data from monitoring stations, store the data in a central database, perform data processing, and detect anomalies. Additionally, the system offers data visualization through maps and tables. The developed solution consists of three core components: a web server, a web application, and ground stations. A key feature of the software is its ability to handle real-time data aggregation and fill data gaps using custom-built aggregators. This is achieved through real-time data parsing, managed by Celery workers and queued via RabbitMQ. All data is stored in an SQL database, with PostgreSQL and Django frameworks facilitating database management and administration. Python scripts are used to process raw data into user-friendly formats such as AQI indices and graphical representations. The system is designed for seamless deployment across multiple remote servers, ensuring high flexibility and reliability for researchers. This software architecture enables scalable, conflict-free deployment, enhancing the efficiency and accuracy of air quality assessments.
Information System of Air Quality Assessment Based of Ground Stations and Meteorological Data Monitoring
(CEUR-WS Team, Aachen, Germany, 2023) Molodets, Bohdan; Hnatushenko, Volodymyr V.; Boldyriev, Daniil; Bulana, Tetiana
ENG: Monitoring ground stations and collecting meteorological data are essential solutions for assessing air quality. A developed information system can aggregate and process the data obtained. The data is transformed into a unified format and returned through written application programming interfaces (APIs). Client interfaces were created for convenient display of the results. The project infrastructure is designed for easy deployment. The architectural solution for creating the system proposes a toolkit that optimizes system operation when performing complex tasks through asynchronous execution. The use of Docker during deployment provides additional capabilities. To calculate the distribution of emissions in Kryvyi Rih, the CALPUFF model was employed for data processing. The article describes the client part structure and interface description. It also displays the processed data, which is the result of applying a mathematical model to the meteorological and station data.
Information System of Air Quality Assessment Using Data Interpolation from Ground Stations
(CEUR-WS Team, Aachen, Germany, 2023) Molodets, Bohdan; Hnatushenko, Volodymyr V.; Boldyriev, Daniil; Bulana, Tetiana
ENG: Monitoring ground stations is crucial for creating interactive maps that assist in assessing air quality. A developed information system can aggregate and process the data obtained, which is then transformed into a unified format and used as input data for interpolation methods that create raster imagery. After processing, the data is stored in Amazon Simple Storage Service or database and can be retrieved using application program interfaces (APIs). The proposed architectural solution for creating the system includes a toolkit that can work with different volumes of data with ease. Using Docker during deployment provides additional capabilities for creating a flexible and scalable system. Specific tools such as PostGis and Geospatial Data Abstraction Library (GDAL) simplify the processing of data. For instance, GDAL helps with the interpolation, cropping, and tiling of the air quality raster image. The article describes the structure of the client part and the interface in detail. By using the Mapbox Graphics Library system, the system can easily visualize big data as a vector layer, helping users recognize hazardous zones and find safe places.
Information System to Enhance Agricultural Production Efficiency Based on Sustainable Development Principles
(CEUR-WS Team, Aachen, Germany, 2024) Hnatushenko, Volodymyr V.; Shuleshko, Victor; Bulana, Tetiana; Molodets, Bohdan
ENG: The increasing global demand for sustainable agricultural practices necessitates innovative solutions to optimize resource use and minimize environmental impact. This paper presents the information technology developed to automate and optimize the operation of a hydroponic system to improve energy and resource efficiency in sustainable agriculture. An automated control platform at the system's base continuously monitors key environmental parameters within the hydroponic facility, including water level, nutrient distribution, lighting, and energy consumption. The system can precisely regulate the processes by collecting real-time data on these variables, providing ideal conditions for plant growth. The hydroponic system is equipped with a set of sensors and actuators that control water flow, fertilizer supply, and lighting according to the needs of each plant. The developed spectral composition is based on combining the spectra of HPS and two peaks of photosynthetic efficiency. This combination of the spectrums complements each other, which allows photosynthesis to proceed as efficiently as possible. The system makes it possible to save up to 25% of electricity consumption without losing quality and quantity characteristics, to reduce the number of hours of illumination, and to be placed in cost-effective climatic conditions.
Information Technologies in IT Education as a Factor of Digitalization of Ukrainian Society
(CEUR-WS Team, Aachen, Germany, 2024) Sokolova, Natalya; Hnatushenko, Volodymyr V.
ENG: The article investigates the digitization of Ukrainian society, the role of digital skills in labor market demand, digital literacy among the population, and the key audience of educators - teenagers. It outlines the approaches and experiences of the Department of Information Technology and Computer Engineering at Dnipro University of Technology in transforming IT education. It specifically examines the teaching of programming courses to first-year students to build foundational competencies for continued learning and the use of digital technologies in training specialists for government bodies. The article also highlights the integration of Cisco International Network Academy courses into the curriculum and the training of specialists through educational partnership projects with businesses.
Information Technology for Detecting Forest Fire Contours Using Optical Satellite Data
(Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро, 2023) Kashtan, Vita; Hnatushenko, Volodymyr V.
ENG: The number of forest fires has increased significantly over the past ten years. It indicates that forest area estimates fires are a very urgent task today. The use of satellite-based data simplifies the process of assessing forest fires. The aim is to develop an information technology for automated forest fire contours detection on digital optical satellite datas in conditions of non-stationarity and uncertainty based on convolutional neural networks. The most popular tools for forest fire analysis are considered. This work proposed using hotspots to identify all fire and smoke pixels for automated forest fire contour detection. It made it possible to obtain contour polygons of the corresponding areas with various attributes: position, size, etc. The results are tested on Sentinel 2 satellite images of the Бvila region. The proposed method has an overall accuracy of 94.3% for the selection of forest fires.
Intelligent Sentinel Satellite Image Processing Technology for Land Cover Mapping
(Dnipro University of Technology, Dnipro, 2024) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Laktionov, I. S.; Diachenko, H. H.
ENG: Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.
Intelligent Technology for Land Cover Monitoring due to Amber Mining on Optical Satellite Images
(Dnipro University of Technology, Dnipro, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Purpose. This article proposes to develop an intelligent technology for detecting land cover changes due to amber mining based on Sentinel-2 optical satellite images. Methodology. The presented intelligent technology combines methods of geometric and radiometric correction, the Dark Object Subtraction algorithm, a hybrid architecture of convolutional neural networks (CNN + EfficientNet-Edge), and an algorithm for detecting changes over time based on the symmetric difference of changed pixels, which ensures high accuracy and efficiency in the process of analysing land cover changes. Findings. The effectiveness of the proposed technology was assessed using the F1-score, Recall, Precision, and Accuracy metrics. The values of the recall and F1-score metrics (92 %) confirm the ability of the system to detect the boundaries of land cover disturbance zones accurately. In addition, the accuracy (94 %), recall (90 %), and overall accuracy (93 %) confirm the ability of the model to effectively classify both mining-impacted areas and areas without signs of anthropogenic interference with a minimum number of errors. The low value of land cover change segmentation error (4.7 %) indicates the high quality of spatial interpretation of the results. Originality. The paper proposes a comprehensive use of convolutional neural networks with the EfficientNet-Edge architecture to detect land cover changes due to amber mining. This approach overcomes the limitations of traditional feedforward neural networks associated with problems of incorrect initialization and suboptimal distribution of weight coefficients. In particular, a comprehensive use of two feature processing levels is proposed: the first one processes simple texture features obtained using average pooling in the CNN architecture, and the second one processes spectral features formed from a 4D tensor at the last convolutional layer of EfficientNet-Edge. This helps to overcome the instability of the training process and improve the ability of the model to detect land cover changes caused by hydromechanical amber mining accurately. An annotation of areas resulting from amber mining and areas without signs of anthropogenic interference was developed. Practical value. The developed technology has practical value for determining changes in the land cover caused by hydromechanical extraction of amber. The theoretical results obtained allow for an effective assessment of the scale of changes and facilitate informed management decisions in environmental protection.
Machine Learning Approaches for Evaluating Forest Fire Impacts on Sentinel-2 Satellite Imagery Across Ukraine
(WUST Publishing House, Wrocław, 2024) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Soldatenko, Dmytro V.; Heipke, Christian
ENG: Forest fires have long-term consequences and serious ecological, social, and economic implications. Utilizing multispectral imagery from the Sentinel-2 satellite, we propose an algorithm based on machine learning models for the detection of burnt forest areas. A new dataset on forest fires has been created, suitable for semantic segmentation models. The proposed algorithm uses an approach based on convolutional neural networks (CNN). The results are analyzed and compared in terms of the intersection over union (IoU) score. The proposed algorithm was tested on Sentinel satellite images acquired in October 2022 for the Kinburn Peninsula, Ukraine, to have an accuracy in terms of IoU of 95%.
Machine Learning for Automatic Extraction of Water Bodies Using Sentinel-2 Imagery
(National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2024) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.
ENG: Context. Given the aggravation of environmental and water problems, there is a need to improve automated methods for extracting and monitoring water bodies in urban ecosystems. The problem of efficient and automated extraction of water bodies is becoming relevant given the large amount of data obtained from satellite systems. The object of study is water bodies that are automatically extracted from Sentinel-2 optical satellite images using machine learning methods. Objective. The goal of the work is to improve the efficiency of the process of extracting the boundaries of water bodies on digital optical satellite images by using machine learning methods. Method. The paper proposes an automated information technology for delineating the boundaries of water bodies on Sentinel-2 digital optical satellite images. The process includes eight stages, starting with data download and using topographic maps to obtain basic information about the study area. Then, the process involved data pre-processing, which included calibrating the images, removing atmospheric noise, and enhancing contrast. Next, the EfficientNet-B0 architecture is applied to identify water features, facilitating optimal network width scaling, depth, and image resolution. ResNet blocks compress and expand channels. It allows for optimal connectivity of large-scale and multi-channel links across layers. After that, the Regional Proposal Network defines regions of interest (ROI), and ROI alignment ensures data homogeneity. The Fully connected layer helps in segmenting the regions, and the Fully connected network creates binary masks for accurate identification of water bodies. The final step of the method is to analyze spatial and temporal changes in the images to identify differences, changes, and trends that may indicate specific phenomena or events. This approach allows automating and accurately identifying water features on satellite images using machine learning. Results. The implementation of the proposed technology is development through Python software development. An assessment of the technology’s accuracy, conducted through a comparative analysis with existing methods, such as water indices and K-means, confirms a high level of accuracy in the period from 2017 to 2023 (up to 98%). The Kappa coefficient, which considers the degree of consistency between the actual and predicted classification, confirms the stability and reliability of our approach, reaching a value of 0.96. Conclusions. The experiments confirm the effectiveness of the proposed automated information technology and allow us to recommend it for use in studies of changes in coastal areas, decision-making in the field of coastal resource management, and land use. Prospects for further research may include new methods that seasonal changes and provide robustness in the selection and mapping of water surfaces.
A Neural Network Approach to Semantic Segmentation of Vehicles in Very High Resolution Images
(National University "Zaporizhzhia Polytechnic", Zaporizhzhia, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Udovyk, Iryna M.; Kazymyrenko, O. V.; Radionov, Y. D.
ENG: Context. The semantic segmentation of vehicles in very high resolution aerial images is essential in developing intelligent transportation systems. It allows for the automation of real-time traffic management and the detection of congestion and emergencies. Objective. This work aims to develop and evaluate the effectiveness of a neural network approach to semantic segmentation in very high resolution aerial images, which provides high detail and correct reproduction of object boundaries. Method. The DeepLab architecture with ResNet-101 as a backbone is used for gradient preservation and multiscale feature analysis. We trained on DOTA data and retrained on specialized sets with classes: vehicles, green areas, buildings, and roads. A loss function based on the Dice coefficient was applied to reduce the imbalance of classes. It effectively solves the class imbalance problem and improves the accuracy of segmenting objects of different sizes. Using ResNet-101 instead of Xception in the backbone network allows us to maintain the gradient as the network depth increases. Results. Experimental studies have confirmed the effectiveness of the proposed approach, which achieves a segmentation accuracy of more than 90%, outperforming existing analogs. The use of multiscale feature analysis allows for preserving the texture features of objects, reducing false classifications. A comparative study with U-Net, SegNet, FCN8s, and other methods confirms the higher performance of the proposed approach in terms of mIoU (82.3%) and Pixel Accuracy (95.1%). Conclusions. The experiments confirm the effectiveness of the proposed method of semantic segmentation of vehicles in ultrahigh spatial resolution images. Using DeepLab v3+ResNet-101 significantly improves the quality of vehicle segmentation in an urbanized environment. Excellent metric performance makes it promising for infrastructure monitoring and traffic planning tasks. Further research will focus on adapting the model to new datasets.
Neural Network-Based Analysis of Forest Fire Aftermath in Class-Imbalanced Remote Sensing Earth Image Classification
(ISPRS, Hannover, Germany, 2024) Hnatushenko, Viktoriia V.; Hnatushenko, Volodymyr V.; Soldatenko, Dmytro V.
ENG: Today's agricultural sector is characterized by an important role of accurate mapping and monitoring of agriculture with the help of satellite imagery, which allows to optimize the use of resources, to plan crop areas and to forecast productivity. Classification of satellite images with unbalanced sample distribution is a critical problem in this regard. Traditional machine learning algorithms in particular have limitations in dealing with sample imbalance. In this paper, we proposed convolution neural networks for semantic segmentation, where sample imbalance is considered based on a particular loss function coupled with data augmentation. To illustrate our method, we use Sentinel-2 remote sensing (RS) images covering a number of regions in Ukraine, and then we create an image dataset of the region and for training and testing make data augmentation. The models with different architectural features were investigated. The results demonstrate that the proposed CNN has a higher classification accuracy than the ones discussed in the paper: the classification accuracy on the test dataset reached 96.7% with intersection-over-union values of up to 89.7%. This opens the way for further research in the direction of refining algorithms for classify satellite data with an imbalanced class structure.
Non-Relational Approach to Developing Knowledge Bases of Expert System Prototype
(Dnipro University of Technology, Ukraine, 2022) Hnatushenko, Volodymyr V.; Hnatushenko, Viktoriia V.; Dorosh, Natalja L.; Solodka, N. O.; Liashenko, O. A.
ENG: Purpose. Use of a non-relational database management system is proposed while developing a database of a prototype of expert system with using a semantic model of the knowledge. Methodology. The study compares traditional relational approach with the proposed non-relational one in terms of the formation of certain queries. The following indices are used to compare efficiency of two management systems for the databases: particular query set (in MySQL and Cypher languages); runtime for the specified record size (i.e. their processing speed); ease of understanding: and software support of the queries. Findings. It has been identified that the graph model is a more expedient solution in the process of designing semantic networks and their development where complex hierarchical relationships between objects have to be stored and processed. Architecture of the graph database has been applied in terms of the specific example. A prototype of an expert system has been developed to demonstrate the capabilities of the created system of logical inference. The classifier of sciences was chosen as an example in the subject area. Originality. A prototype of the expert system, using the proposed non-relational approach, has been designed involving modern service-oriented architecture (SOA). The abovementioned helped separate the database from the inference engine and the user interface, facilitate perception as well as update and code debugging. Service-oriented architecture makes the system more flexible and robust. Practical value. The developed software is meant to develop both simple expert systems and medium-complex ones.
Quantum Machine learning for Fusion of Multichannel Optical Satellite Images
(Dnipro University of Technology, Dnipro, 2025) Kashtan, Vita Yu.; Hnatushenko, Volodymyr V.; Wereszczyński, Kamil; Cyran, Krzysztof
ENG: Purpose. To develop a novel approach for fusion of optical satellite images based on machine learning and quantum optimization for integrating spatial-spectral information from RGB and IR channels. Methodology. The proposed approach involves sequential processing of input data, including geometric, radiometric, and atmospheric corrections. Each channel is decomposed into low-frequency and high-frequency components using a Gaussian filter. The Independent Component Analysis (ICA) method is applied to reduce the dimensionality of input data. A quantum optimizer approximation algorithm is applied to analyze the infrared channel. A deep convolutional neural network with residual dense blocks is used to extract spatial structural features from RGB channels. After integrating features through fully connected layers, the quantum block optimizes the weight coefficients for the final channel fusion. Findings. Quantitative evaluation demonstrates that the proposed approach outperforms classical fusion methods, including Brovey, Gram-Schmidt, IHS, HCS, HPFC, ATWT, PCA, and CNN, in spectral and spatial information integration accuracy. The method achieves the lowest mean squared error (MSE = 191.8), high structural similarity index (SSIM = 0.43), entropy (Entropy = 7.54), and Sobel filter range (Sobel Sharp = 19.19–21.67 across R, G, B channels). Visual analysis also confirms qualitative advantages: images exhibit clear structure without artifacts and balanced color reproduction consistent with the spectral characteristics of the original RGB data. Originality. A novel approach to utilizing information of the IR channel is proposed, which integrates a quantum-classical algorithm within a deep convolutional neural network architecture for synergistic processing of multichannel optical images using multilevel frequency decomposition and a weighted feature fusion mechanism. Practical value. The proposed approach can be implemented in Earth remote sensing systems to enhance the quality of satellite image processing, particularly for mapping, land resource monitoring, agricultural control, and environmental analysis tasks. Applying quantum algorithms opens new opportunities for improving efficiency and accuracy in processing multidimensional geoinformation data containing IR channel information.
Raster image processing using 2D Padé-type approximations
(IOP Publishing, 2023) Olevskyi, V. I.; Olevska, Yu. B.; Olevskyi, O. V.; Hnatushenko, Volodymyr V.
ENG: We have developed a method called the two-dimensional Padé-type approximants method, which can be used to reduce the Gibbs phenomenon in the harmonic two-dimensional Fourier series. This method can be applied to both monochrome and color raster images. To do this, we implement the generalized two-dimensional Padé approximation proposed by Chisholm. In this approach, we select the range of frequency values on the integer grid according to the Vavilov method. We propose a definition of a Padé-type functional and provide examples of its application to simple discontinuous templates represented as raster images. Through this study, we are able to draw conclusions about the practical usage and advantages of the Padé-type approximation. We demonstrate that the Padé-type approximant effectively eliminates distortions associated with the Gibbs phenomenon, and it is visually more appropriate than the Fourier approximant. Additionally, the application of the Padé-type approximation reduces the number of parameters without sacrificing precision.
Simulation-Driven Assessment of Cryptographic Algorithms for Resource-Constrained Infocommunication Networks
(Dnipro University of Technology, Dnipro, 2025) Laktionov, I. S.; Hnatushenko, Volodymyr V.; Udovyk, Iryna M.; Olevskyi, V. I.
ENG: Purpose. To conduct a multi-criteria evaluation and analysis of the performance of encryption algorithms that may be potentially resistant to contemporary cyberattacks, including quantum attacks. The evaluation takes into account the ability of the algorithms to be deployed on devices with limited computational resources within the infocommunication networks during the transmission of information messages. Methodology. Software implementation, testing and validation of selected cryptographic algorithms based on Python, considering the impact of limited resources and destabilising factors, such as signal noise components, based on computer experiments were applied. The performance of the studied cryptographic algorithms was analysed using statistical data processing methods and a multi-criteria evaluation approach. Findings. The symmetric algorithms AES-256-GCM and ChaCha20-Poly1305 demonstrated the highest accuracy in signal recovery following encryption and decryption (MSE ranges from 1.95 · 10-6 to 5.12 · 10-5). The time taken to encrypt and decrypt I/Q signals using symmetric algorithms was found to be around 2.5 times faster than that required by the Kyber family. Computer experiments confirmed the existence of a trade-off between processing speed and security level. Symmetric algorithms are optimal for scenarios with critical processing speed requirements. However, Kyber provides greater protection reliability, albeit at the cost of additional resources. The correctness of the proposed computer model, which enables the computational and information-functional characteristics of cryptographic algorithms to be evaluated, has been proven. Originality. Patterns of the destabilising influence of signal-to-noise ratio indicators and signal length on the accuracy of digital signal recovery after encryption have been established for different cryptographic algorithms (AES, ChaCha20 and the Kyber) in the context of their use in resource-constrained infocommunication systems. Practical value. Implementing the computer model proved its suitability for studying cryptographic algorithms in resource-constrained environments, as well as its potential for improving information security protocols and selecting optimal algorithms based on processing speed requirements and desired security levels.
The Use of Generative Artificial Intelligence in Software Testing
(Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро, 2024) Hnatushenko, Volodymyr V.; Pavlenko Iegor V.
ENG: This article explores the potential of using generative artificial intelligence (AI) for software testing, reflecting on both the advantages and potential drawbacks of this emerging technology. Considering the vital role of rigorous testing in software production, the authors ponder whether generative AI could make the testing process more efficient and comprehensive, without the need to increase resources. The article delves into the current limitations of this technology, emphasizing the need for continuous exploration and adaptation. It concludes with a summation of potential innovative solutions and avenues for future investigation. The paper encourages discussions surrounding the question of fully automated testing and the role of human specialists in the future of QA. It ultimately provides a thought-provoking reflection on the intersection of emerging technologies, and their societal impacts.