Browsing by Author "Soldatenko, Dmytro V."

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    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.
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    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%.
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    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.
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    Study of Efficiency of Using IT-Infrastructure as a Service For Cloud Computing
    (Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», 2022) Soldatenko, Dmytro V.; Gnatushenko, Viktorija V.
    ENG: With the growth of the information technology market and the constant increase in demand, companies began to carry an unprecedented burden on their own infrastructure, trying to meet t customers’ growing expectations. Safe, reliable, and fast services are a top priority for companies that are largely trying to meet the expectations of their customers and adjust to the constant changes in the service market. With constant efforts to increase their own computing power, infrastructure and storage space, companies are increasingly finding that the cost of developing and maintaining a reliable, secure, and at the same time scalable infrastructure is prohibitive. To cope with the challenges of acquiring and maintaining their own infrastructure solutions, companies can take advantage of off-the-shelf solutions such as cloud computing. Cloud computing is a fast-growing industry that allows companies not to focus on expanding their own local infrastructure and, instead, move to the use of ready-made Internet services. Cloud service providers provide access to storage and processing, as well as software at affordable and dynamic prices, which allows companies to save money by adopting cloud solutions. Cloud services provide a variety of service models, each capable of meeting a specific set of business requirements and needs. The main service models include Infrastructure as a Service (IaaS), Software as a Service (SaaS) and Platform as a Service (PaaS), the features and disadvantages of which vary and are interchangeable, allowing you to choose a more suitable model. This article explores existing solutions and services and provides the advantages and disadvantages of using one or another solution for various needs and highlighted the most universal solution suitable for most requests. In the study, the most popular solutions related to cloud computing present and analyze their key features. The most powerful and attractive service for processing a large amount of input data, including space images, is IaaS. When used, it provides high speed and availability of resources, adaptation to the task, data security due to distributed storage and processing, which allows increasing performance and minimizing latency for the end user.