MineCam: Application of Combined Remote Sensing and Machine Learning for Segmentation and Change Detection of Mining Areas Enabling Multi-Purpose Monitoring

Author:

Jabłońska Katarzyna12ORCID,Maksymowicz Marcin3,Tanajewski Dariusz3ORCID,Kaczan Wojciech24,Zięba Maciej1,Wilgucki Marek2

Affiliation:

1. Department of Artificial Intelligence, Faculty of Computer Science and Telecommunications, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

2. Remote Sensing Business Solutions, Jana Długosza 60A, 51-162 Wrocław, Poland

3. Remote Sensing Environmental Solutions, Jana Długosza 60A, 51-162 Wrocław, Poland

4. Department of Mining, Faculty of Geoengineering, Mining and Geology, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Abstract

Our study addresses the need for universal monitoring solutions given the diverse environmental impacts of surface mining operations. We present a solution combining remote sensing and machine learning techniques, utilizing a dataset of over 2000 satellite images annotated with ten distinct labels indicating mining area components. We tested various approaches to develop comprehensive yet universal machine learning models for mining area segmentation. This involved considering different types of mines, raw materials, and geographical locations. We evaluated multiple satellite data set combinations to determine optimal outcomes. The results suggest that radar and multispectral data fusion did not significantly improve the models’ performance, and the addition of further channels led to the degradation of the metrics. Despite variations in mine type or extracted material, the models’ effectiveness remained within an Intersection over Union value range of 0.65–0.75. Further, in this research, we conducted a detailed visual analysis of the models’ outcomes to identify areas requiring additional attention, contributing to the discourse on effective mining area monitoring and management methodologies. The visual examination of models’ outputs provides insights for future model enhancement and highlights unique segmentation challenges within mining areas.

Funder

National Centre for Research and Development

Publisher

MDPI AG

Reference33 articles.

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2. Loh, Y.W., Mohammad, A., Tripathi, A., van Niekerk, E., and Yanto, Y. (2023). Advancing Metals and Mining in Southeast Asia with Digital and Analytics, McKinsey & Company. Available online: https://www.mckinsey.com/industries/metals-and-mining/our-insights/advancing-metals-and-mining-in-southeast-asia-with-digital-and-analytics.

3. Mononen, T., Kivinen, S., Kotilainen, J.M., and Leino, J. (2024, January 10). Social and Environmental Impacts of Mining Activities in the EU 2022. Available online: https://www.europarl.europa.eu/RegData/etudes/STUD/2022/729156/IPOL_STU(2022)729156_EN.pdf.

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5. Nascimento, F.S., Gastauer, M., Souza-Filho, P.W.M., Wilson, R., Nascimento, J., Santos, D.C., and Costa, M.F. (2020). Land Cover Changes in Open-Cast Mining Complexes Based on High-Resolution Remote Sensing Data. Remote Sens., 12.

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