Mining Site Data Extraction Using 3D Machine Learning

Summit Track: 
Technical Insights I

This talk focuses on extracting invariant features for segmentation of 3D models of mining sites. The image data is generated by stitching together geotagged images from a drone. The 3D model is then generated by applying stereo reconstruction using structure from motion. This reconstruction gives a dense 3D model with RGB data corresponding to the point cloud. There are a limited number of features for point clouds. The mining site terrain are mountainous in nature and thus the image loses a lot of information even within the RGB space. In this presentation, we discuss ways to best utilize both 3D and RGB data to extract features with high discriminability and invariance using machine learning. This approach shows accurate segmentation of mining sites. A segmented site can be used to provide key insights, such as ore deposit locations, drill hole deviations and safety and hazard warnings.

Speaker(s):

Ravi Sahu

Founder & CEO, Strayos

Ravi Sahu is a founder and CEO of Strayos. Before founding Strayos, he spent more than 12 years working globally with Fortune 500 companies like AT&T, Verizon and British Telecom in various roles, from product management to the building of teams for large digital transformation projects. His area of expertise is in artificial intelligence and data analytics. He holds an MBA from Washington University in St. Louis and a computer science engineering degree from India.

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