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The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Ghamisi, P.; Rafiezadeh Shahi, K.; Duan, P.; Rasti, B.; Lorenz, S.; Booysen, R.; Thiele, S. T.; Contreras Acosta, I. C.; Kirsch, M.; Gloaguen, R.

The digitization and automation of the raw material sector is required to attain the targets set by the Paris Agreements and support the sustainable development goals defined by the United Nations. While many aspects of the industry will be affected, most of the technological innovations will require smart imaging sensors. In this review, we assess the relevant recent developments of machine learning for the processing of imaging sensor data. We first describe the main imagers and the acquired data types as well as the platforms on which they can be installed. We briefly describe radiometric and geometric corrections as these procedures have been already described extensively in previous works. We focus on the description of innovative processing workflows and illustrate the most prominent approaches with examples. We also provide a list of available resources, codes, and libraries for researchers at different levels, from students to senior researchers, willing to explore novel methodologies on the challenging topics of raw material extraction, classification, and process automatization.

Keywords: Deep learning (DL); Earth observation; machine learning; Mining; Raw materials.

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  • Open Access Logo IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14(2021), 8971-8988
    Online First (2021) DOI: 10.1109/JSTARS.2021.3108049

Publ.-Id: 33864