Automated lithological classification using UAV and machine learning on an open cast mine

Beretta, Filipe Schmitz; Rodrigues, Áttila Leães; Peroni, Rodrigo de Lemos; Costa, João Felipe Coimbra Leite

doi:10.1080/25726838.2019.1578031

Cited by 24 publications

(10 citation statements)

References 19 publications

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“…Deposits located at coastal areas are rather difficult to quantify due to their close proximity with the sea. Air borne methods such as the employment of UAV drones present an unconventional way of exploring and quantifying target resources within this area effectively [22]. A map showing a 30 m × 90 m area from which experiments were conducted in an attempt to apply UAV drone technology in identifying magnetite iron sands is shown in Figure 1.…”

Section: The Study Areamentioning

confidence: 99%

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

et al. 2022

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The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.

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Section: The Study Areamentioning

confidence: 99%

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

et al. 2022

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“…In the field of mineral exploration and targeting, a geochemical anomaly detection [21] study that uses the exploration data to detect geochemical abnormalities was conducted. Geological mapping [22][23][24] studies were conducted using the characteristics of rocks. A mineral analysis [25][26][27][28][29][30][31] was conducted using drilling data or samples.…”

Section: Publication Sourcementioning

confidence: 99%

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Jung¹,

Choi²

2021

Minerals

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Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

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“…Many of these applications classify each point separately based entirely on its spectral characteristics (e.g., by using the random forest method to classify individual point spectra [117]; Fig. 9 ), but a few approaches that incorporate spatial and texture or reflectance information are beginning to emerge [118]- [120]. These provide a first step towards combined spectral-spatial classification of multi or hyperspectral point clouds.…”

Section: A State Of the Artmentioning

confidence: 99%

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

Ghamisi¹,

Shahi²,

Duan³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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Automated lithological classification using UAV and machine learning on an open cast mine

Cited by 24 publications

References 19 publications

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

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

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