Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
Nächste SlideShare
What to Upload to SlideShare
What to Upload to SlideShare
Wird geladen in …3
×
1 von 25

Sandra Lorenz WiMLDS Dresden

0

Teilen

Herunterladen, um offline zu lesen

Sandra Lorenz WiMLDS Dresden, 17 March 2021

Ähnliche Bücher

Kostenlos mit einer 30-tägigen Testversion von Scribd

Alle anzeigen

Ähnliche Hörbücher

Kostenlos mit einer 30-tägigen Testversion von Scribd

Alle anzeigen

Sandra Lorenz WiMLDS Dresden

  1. 1. Helmholtz-Institute Freiberg for Resource Technology · Department of Exploration · Dr. Sandra Lorenz · s.lorenz@hzdr.de · www.hzdr.de The role of data science and machine learning to achieve sustainable mineral exploration
  2. 2. 
 @ EU 2020  2
  3. 3. 
 Challenges ▪ Cost ▪ Timing ▪ Invasiveness ▪ Acceptance ▪ Accessibility ▪ Safety ▪ Scale Gap CONVENTIONAL MINERAL EXPLORATION Photo: Moritz Kirsch Lorenz 2019  3
  4. 4. 
 E.g., Spectral Imaging for surface mapping of material characteristics ▪ non-invasive ▪ versatile ▪ rapid ▪ multi-scale ▪ multi-view NON-INVASIVE EXPLORATION TECHNOLOGY Lorenz 2019 Photos: HZDR-HIF  4
  5. 5. 
 SPECTRAL IMAGING FOR RESOURCE CHARACTERIZATION Lorenz 2019  5
  6. 6. 
 SPECTRAL IMAGING FOR RESOURCE CHARACTERIZATION Lorenz 2019, Booysen et al. 2021  6
  7. 7.
  8. 8. 
  8
  9. 9. 
 200 mm HYPERSPECTRAL DRILL CORE MAPPING Photos: HZDR-HIF
  10. 10. MULTI-SENSOR APPROACH 200 mm  10
  11. 11. 
 CLASSIFICATION Lorenz et al., 2021  11
  12. 12. 
 MINERAL FEATURE MAPPING  12
  13. 13. 
 AUTOMATED REAL-TIME PROCESSING  13
  14. 14. 
 AUTOMATED REAL-TIME PROCESSING  14
  15. 15.  15
  16. 16. 
 DIGITAL OUTCROPS AND OPEN MINE PIT MAPPING Photos: M. Kirsch  16
  17. 17. 
 2D image space vs. 3D point cloud space THE NEED FOR MULTIMODAL 3D PROCESSING 3D MULTIMODAL POINT CLOUD The world is 3D, so AI should see it in 3D too! ❖ major advances in image processing and AI (feature extraction, denoising, classification, ...) ❖ distorted spatial relations ❖ occlusions ❖ joining spatial features (shape, texture) with multimodal data characteristics (spectra, ..) ❖ object detection and segmentation ❖ limited AI implementation for monomodal point clouds (3D- CNN, PointNet) 2D SPECTRAL IMAGING
  18. 18. 
 Thiele et al. 2021 (under rev.) CLASSIFICATION OF MULTIMODAL 3D DATA
  19. 19. 
 DRONE-BORNE HIGH- RESOLUTION MAPPING OF REMOTE, INACCESSIBLE AREAS Jakob et al., 2017 Photos: HZDR-HIF
  20. 20. 
 (offset for clarity) wavelength (nm) raw corrected with topographic correction uncorrected GEOMETRIC CORRECTION RADIOMETRIC CORRECTION COMPLEX DATA CORRECTIONS Lorenz 2019  20
  21. 21. 
 DRONE-BORNE HIGH-RESOLUTION MAPPING OF RARE EARTH ELEMENTS Booysen et al., 2021
  22. 22. 
 DRONE-BORNE ENVIRONMENTAL MONITORING e.g., mapping of Acid Mine Drainage Flores et al., 2021 50 mm  22
  23. 23. 
 OUTLOOK: AUTONOMOUS DRONE SWARMS  23
  24. 24. 
 ✓ Enhance worker safety by autonomous processes ✓ Reduce mapping costs ✓ Reduce drilling ✓ Enhance mapping accuracy, speed and coverage ✓ Reduce waste and ore processing effort by better preselection ✓ Environmental impact monitoring AI-BOOSTED NON-INVASIVE EXPLORATION TECHNOLOGY  24
  25. 25. 
 THANK YOU!

×