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Challenges to Large Scale Mapping: Can Data Geometry Help?

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Quantifying Error in Training Data for Mapping and Monitoring the Earth System - A Workshop on “Quantifying Error in Training Data for Mapping and Monitoring the Earth System” was held on January 8-9, 2019 at Clark University, with support from Omidyar Network’s Property Rights Initiative, now PlaceFund.

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Challenges to Large Scale Mapping: Can Data Geometry Help?

  1. 1. ORNL is managed by UT-Battelle, LLC for the US Department of Energy Challenges to Large Scale Mapping: can data geometry help? Presented at Clark University 2019 Dalton Lunga AI and Machine Learning Scientist Geographic Data Sciences Group lungadd@ornl.gov
  2. 2. 22 Area: 1,345,006 sq km Images: 20,000 Data size: 90TB Total 7.68 trillion px Builtup: 51 billion px Area: 1,262,155 sq km Images: 4800 Data size: 18TB Builtup: 62 billion px Total 32 trillion px Data volume challenge only 2% of imagery covers built-up area … we are searching for a needle in a haystack!
  3. 3. 33 From 1m NAIP imagery From <1m WV and aerial imagery Data Variety: Heterogeneity of sensors … introduces sample data bias …
  4. 4. 44 Long Beach, LA
  5. 5. 55 Humanitarian impact: mission support for 2017 hurricane season
  6. 6. 66 Generating high resolution solar panel maps for US Cities San Francisco
  7. 7. 77 Humanitarian impact: high resolution settlement maps to support polio vaccination in Nigeria Kano Borno EnuguLagos
  8. 8. 88 Site A Site B Site C Site D Challenges for automation: data distribution shifts
  9. 9. 99 Data bias from measurement distortions
  10. 10. 1010 Limitations for automated classifiers
  11. 11. 1111 Exploiting geometry of data
  12. 12. 1212 Understanding human labeling induced errors
  13. 13. 1313 Shifted labels—binary model output 3 pixels 6 pixels 12 pixels 15 pixels The level of degraded quality of training annotation for building mapping
  14. 14. 1414 False negative—distance model output 10 % 20% 40% 60 % The level of degraded quality of training annotation for building mapping
  15. 15. 1515 False positive—binary model output 10 % 20% 40% 60 % The level of degraded quality of training annotation for building mapping
  16. 16. 1616 Thank you Dalton Lunga lungadd@ornl.gov Budhu Bhaduri bhaduribl@ornl.gov Contact Details
  17. 17. 1717 Active learning to increase sample set • Human-in-the-loop: Can we incorporate more representative/meaningful samples into the training set, with less human efforts with the most valuable domain knowledge encodedà Machine guided and human assisted labeling
  18. 18. 1818 Data Bias from Training Data Sampling

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