Sarvesh Kumar Singh

About

Currently working as an Automation engineer at Anglo American's Aquila mine (underground coal). I have actively engaged in remote sensing over the past eight years, with a specific focus on light detection and ranging (LiDAR) based simultaneous localisation and mapping systems. My expertise extends to sensor/data fusion, algorithm development and the application of machine learning techniques, specifically tailored for processing large-scale 3D geospatial data. After completing my PhD, which centred on optimising mobile laser scanning for underground mines, I made a strategic transition into industry. This decision was motivated by a desire to witness the practical implementation of my research findings and contribute to the advancement of automation within the mining sector.

Employment

• Anglo American Metallurgical Coal Graduate Automation Engineer
  2022 - Present
• University of New South Wales Postdoctoral Fellow
  2021 - 2022
• University of New South Wales PhD candidate
  2018 - 2022

Education

• The University of New South Wales Doctor of Philosophy (PhD)
  2018 - 2022

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• Spoil characterisation using UAV-based optical remote sensing in coal mine dumps
  International Journal of Coal Science & Technology 2023 DOI: 10.1007/s40789-023-00622-4
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• Evaluation of Segmentation Methods for Spoil Pile Delineation Using UAV Images
  IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium 2023 DOI: 10.1109/igarss52108.2023.10283351
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• A review of laser scanning for geological and geotechnical applications in underground mining
  International Journal of Mining Science and Technology 2023 DOI: 10.1016/j.ijmst.2022.09.022
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• Automated rock mass discontinuity set characterisation using amplitude and phase decomposition of point cloud data
  International Journal of Rock Mechanics and Mining Sciences 2022 DOI: 10.1016/j.ijrmms.2022.105072
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• Automated structural discontinuity mapping in a rock face occluded by vegetation using mobile laser scanning
  Engineering Geology 2021 DOI: 10.1016/j.enggeo.2021.106040
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• A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner
  International Journal of Mining Science and Technology 2021 DOI: 10.1016/j.ijmst.2021.01.001
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• Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environment
  2021
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• Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial neural network
  International Journal of Remote Sensing 2021 DOI: 10.1080/2150704x.2020.1809734
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• A Coarse-to-Fine Approach for Rock Bolt Detection From 3D Point Clouds
  IEEE Access 2021 DOI: 10.1109/access.2021.3120207
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• A combined approach for estimating horizontal principal stress magnitudes from borehole breakout data via artificial neural network and rock failure criterion
  International Journal of Rock Mechanics and Mining Sciences 2020 DOI: 10.1016/j.ijrmms.2020.104539
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