Tailings Pond Classification Based on Satellite Images and Machine Learning: An Exploration of Microsoft ML.Net

Haoxuan Yu and Izni Zahidi ()
Additional contact information
Haoxuan Yu: Civil Engineering Discipline, School of Engineering, Malaysia Campus, Monash University, Bandar Sunway 47500, Malaysia
Izni Zahidi: Civil Engineering Discipline, School of Engineering, Malaysia Campus, Monash University, Bandar Sunway 47500, Malaysia

Mathematics, 2023, vol. 11, issue 3, 1-14

Abstract: Mine pollution from mining activities is often widely recognised as a serious threat to public health, with mine solid waste causing problems such as tailings pond accumulation, which is considered the biggest hidden danger. The construction of tailings ponds not only causes land occupation and vegetation damage but also brings about potential environmental pollution, such as water and dust pollution, posing a health risk to nearby residents. If remote sensing images and machine learning techniques could be used to determine whether a tailings pond might have potential pollution and safety hazards, mainly monitoring tailings ponds that may have potential hazards, it would save a lot of effort in tailings ponds monitoring. Therefore, based on this background, this paper proposes to classify tailings ponds into two categories according to whether they are potentially risky or generally safe and to classify tailings ponds with remote sensing satellite images of tailings ponds using the DDN + ResNet-50 machine learning model based on ML.Net developed by Microsoft. In the discussion section, the paper introduces the environmental hazards of mine pollution and proposes the concept of “Healthy Mine” to provide development directions for mining companies and solutions to mine pollution and public health crises. Finally, we claim this paper serves as a guide to begin a conversation and to encourage experts, researchers and scholars to engage in the research field of mine solid waste pollution monitoring, assessment and treatment.

Keywords: mine geology; computational intelligence; remote sensing; environment management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/517/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/517/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:517-:d:1039770

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().