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Intelligent technology for land cover monitoring due to amber mining on optical satellite images

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Category: Content №3 2025

Last Updated on 25 June 2025

Published on 30 November -0001

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Authors:

V.Yu.Kashtan, orcid.org/0000-0002-0395-5895, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Hnatushenko*, orcid.org/0000-0003-3140-3788, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (3): 156 - 164

https://doi.org/10.33271/nvngu/2025-3/156

Abstract:

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.

Keywords: land cover change map, spectral features, textural features, EfficientNet-Edge, deep learning, convolutional neural networks, optical satellite images

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