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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 4, pp. 89-102

Detection of dredge tailings dumps in satellite imagery using computer vision and machine learning methods

R.A. Sekrieru 1 , K.S. Tsygulev 1 , S.A. Orlov 1 , S.I. Smagin 1 , T.V. Kozhevnikova 1 
1 Computing Center FEB RAS, Khabarovsk, Russia
Accepted: 11.06.2026
DOI: 10.21046/2070-7401-2026-23-4-89-102
The paper presents the results of a study of the potential of automated detection of areas containing dredge tailings from gold mining using satellite data and methods of computer vision and machine learning. The relevance of the study stems from the demand for assessing the reserves of technogenic raw materials in remote and hard-to-reach areas for potential reprocessing. The study uses multispectral imagery from the Sentinel-2 satellites covering the territory of the Far Eastern Federal District of Russia for the period of 2018–2024. Two approaches to solving the problem are considered: a pixel-based approach based on fully connected neural networks and random forest models, and an object-based approach using convolutional and transformer neural networks. An analysis of the informativeness of spectral bands was carried out using gradient-weighted attribution, which made it possible to optimize the input data of the models. To delineate individual tailings within the identified areas, the use of the Frangi filter is proposed that detects linear structures characteristic of dredge tailings.
Keywords: remote sensing of the Earth, gold mining, mine tailings, dredging method, computer vision, machine learning
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