Статті КІТС (ДМетІ)

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

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Applying Machine Learning Techniques to Analyze Forest Fire Impacts on Sentinel-2 Imagery across Ukraine
(Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро, 2026) Hnatushenko Viktoriia V.; Udovyk, Iryna M.; Heipke, Christian; Hnatushenko, Maksym V.
ENG: Forest fires pose severe ecological and socio-economic threats, necessitating efficient tools for rapid damage assessment. This study presents a machine learning approach for detecting burnt forest areas in Ukraine using multispectral Sentinel-2 imagery. A new manually annotated dataset was developed for training semantic segmentation models, addressing the scarcity of open data for the region. The proposed convolutional neural network, based on an encoder–decoder architecture with Xception blocks, effectively captures spectral patterns associated with fire damage. Experiments conducted on Sentinel-2 Level-2A imagery of the Kinburn Peninsula (October 2022) demonstrate high detection performance, achieving an Intersection over Union (IoU) of 95%. The results confirm the model’s capability for accurate burnt-area mapping and highlight its potential for broader applications in regional fire monitoring and environmental management.
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.
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.
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%.