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

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.

Figure 1. Geology map of an open-pit mine in southern Spain (Corta Atalaya, Rio Tinto) created by applying an RF classifier to hypercloud data [116]. The hypercloud approach allowed the fusion of ten ground-based and 357 UAV-based hyperspectral images while simultaneously mitigating distortions and facilitating subsequent 3-D geological interpretation and modeling. Colored circles show the locations, where hand samples were collected and used to train the classifier



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