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Space Edition, Kalev Koppel, KappaZetta

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Space Edition, Kalev Koppel, KappaZetta

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Space Edition, Kalev Koppel, KappaZetta

  1. 1. Deep learning for grasslands mowing detection based on Sentine-1/-2 time-series Kalev Koppel Mobile Monday, 12.11.2018, Tartu 1
  2. 2. • Established in 2015 • Experience in SAR, GIS, IT • EO applications for the agricultural segment • Cooperation with Estonian, Swedish and Danish paying agencies (PAs) • Research since 2011, developed a method based on interferometric coherence Company
  3. 3. Subsidy checks use case • EU CAP • 50 billion € budget (39% of total EU budget) • Paying agencies (PA) in each EU state • PA - responsible for honest sharing of the money, verifying subsidy claims • Rule of on-the-spot-checks 3 An isolated grassland – status “not mowed”
  4. 4. Automated country-wide mowing detection Business operations: • Mowing detection system for Estonian PA, operational in 2018 • Pilots with Swedish and Danish PA-s Features: • 90% detection accuracy • 95% coverage • < 24 hrs status update
  5. 5. 5 7TB of satellite images, Estonia 2017
  6. 6. Mowing detection is a piece of cake
  7. 7. Mowing detection is a piece of cake
  8. 8. • Caps in the data • Outliers in the data • Tricky weather - precipitation • Seasonal patterns, 2017 vs 2018 not like two drops of water • Different agricultural practices • Limited training/validation data • Size and shape of field parcels 8 Lesson 1 – time-series analysis not easy
  9. 9. 9 Lesson 2 – interpreting accuracy
  10. 10. 10 Lesson 3 – garbage in, garbage out
  11. 11. • Unstable external services • Changing product formats • Issues of the 3rd party or open source software • Performance issues • Technical dept - additional rework caused by choosing an easy solution now 11 Lesson 4 - software development not easy
  12. 12. 12 Operational services
  13. 13. Questions? Kalev Koppel kalev.koppel@kappazeta.ee +372 515 9966 www.kappazeta.ee

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