Szczegóły publikacji
Opis bibliograficzny
The potential of U-Net in detecting mining activity: accuracy assessment against GEE classifiers / Beata HEJMANOWSKA, Krystyna MICHAŁOWSKA, Piotr KRAMARCZYK, Ewa GŁOWIENKA // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417 . — 2025 — vol. 15 iss. 17 art. no. 9785, s. 1–37. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 35–37, Abstr. — Publikacja dostępna online od: 2025-09-05
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162083 |
|---|---|
| Data dodania do BaDAP | 2025-09-22 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app15179785 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
Illegal mining poses significant environmental and economic challenges, and effective monitoring is essential for regulatory enforcement. This study evaluates the potential of the U-Net deep learning model for detecting mining activities using Sentinel-2 satellite imagery over the Strzegom region in Poland. We prepared annotated datasets representing various land cover classes, including active and inactive mineral extraction sites, agricultural areas, and urban zones. U-Net was trained and tested on these data, and its classification accuracy was assessed against common Google Earth Engine (GEE) classifiers such as Random Forest, CART, and SVM. Accuracy metrics, including Overall Accuracy, Producer’s Accuracy, and F1-score, were computed. Additional analyses compared model performance for detecting licensed versus potentially illegal mining areas, supported by integration with publicly available geospatial datasets (MOEK, MIDAS, CORINE). The results show that U-Net achieved higher detection accuracy for mineral extraction sites than the GEE classifiers, particularly for small and spatially heterogeneous areas. This approach demonstrates the feasibility of combining deep learning with open geospatial data for supporting mining activity monitoring and identifying potential cases of unlicensed extraction.
