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Recognition of arable lands on the territory of Samara region using satellite images for solving land use problems
A.Y. Bavrina 1, A.A. Agafonov 1

1 Samara National Research University,
443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 2227 kB

DOI: 10.18287/2412-6179-CO-1754

Pages: 1002-1011.

Full text of article: Russian language.

Abstract:
The paper presents a technology for recognizing arable land from remote sensing images to solve land use problems at the regional level of the Russian Federation. The application of modern deep learning methods to identify the arable land boundaries from both single and a series of medium-resolution Sentinel-2 images is being investigated. According to research, the best quality can be achieved using the UPerNet architecture when extracting multiscale features using Swin Transformer v2 algorithm. The resulting vector layer of arable land is used to solve the problem of detecting illegal plowing of specially protected natural areas. The work makes a significant contribution to improving the efficiency of regional natural resource management systems, demonstrating how the use of artificial intelligence and remote sensing images helps to automate the solution of land use problems.

Keywords:
Earth remote sensing images, arable land, land use, protected areas, deep neural networks, semantic segmentation.

Citation:
Bavrina AY, Agafonov AA. Recognition of arable lands on the territory of Samara region using satellite images for solving land use problems. Computer Optics 2025; 49(6): 1002-1011. DOI: 10.18287/2412-6179-CO-1754.

Acknowledgements:
This work was financially supported by the Russian Science Foundation under project No. 23-11-20013.

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