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Large Scale Datasets for Analytics on Satellite Imagery
This document discusses Planet's efforts to build large datasets for object detection in satellite imagery to support applications in areas like disaster response, infrastructure monitoring, and defense. It describes Planet's collection of satellite imagery from multiple constellations at different resolutions. It also outlines Planet's process to build a disaster dataset through automated data collection and filtering, crowd and expert annotation of chips, and evaluation of baseline models to characterize performance.
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Large Scale Datasets for Analytics on Satellite Imagery
- 1. Kure Atoll, Hawaii,USA – May 12, 2016 Large Scale Datasets for Object Detection in Satellite Imagery Gopal Erinjippurath, Senior Director, Analytics Engineering 14 Nov 2018
- 2. IN THE SPANOF A DAY OR WEEK OR MONTH… An illegal logging road appears in the rainforest... and 1,000-year-old trees are lost forever. IN THE SPAN OF A DAY OR WEEK OR MONTH…
- 3. A plane departs from a location of interest — and a military loses situational awareness.
- 4. IN THE SPANOF A DAY OR WEEK OR MONTH… A community is devastated by a natural disaster... and the local government lacks the ability to deploy emergency responders effectively.
- 5. MISSION To image thewhole world every day, making change visible, accessible and actionable.
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- 8. VIDEO PLACEHOLDER NEW LINESCANNER VIDEO
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- 10. ENERGY & INFRASTRUCTUREDEFENSE& INTELLIGENCE FORESTRY FINANCE & BUSINESS INTELLIGENCEINSURANCE EMERGENCY MANAGEMENT CIVIL GOVERNMENT AGRICULTURE MAPPING
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- 12. Web Platform forGeospatial Analytics Planet Doves RapidEye SkySat (TerraBella) Landsat 8 Sentinel 2 Customer Data GEOSPATIAL DATA AUTOMATED DATA PIPELINE & ANALYTICS APPLICATIONS Analytics Customer Algorithms Image Processing 3rd Party Applications Direct Data Access Via Web
- 13. Building a DisastersDataSet
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- 15. Example Disaster AOI(Hurricane Irma, Sept 2017)
- 16. SkySat Scene Strip PlanetScopeScene (ISO orbit) PlanetScope Scene (SSO orbit) SkySat Sample (Harbor) Example Disaster AOI (Hurricane Irma, Sept 2017)
- 17. Example Disaster AOI(Hurricane Irma, Sept 2017)
- 18. Disaster AOIs Public Dataset OSM Filtering Scene Level Sampling Chipping Crowd Annotation Object specific Instructions and test Qs Expert Curation Expert referenceand training set Gold Standard Dataset Chip-level Post Filtering Public Dataset Data Collection Funnel
- 19. Disaster AOIs Public Dataset OSM Filtering Scene Level Sampling Chipping Crowd Annotation Object specific Instructions and test Qs Expert Curation Expert referenceand training set Gold Standard Dataset Chip-level Post Filtering Public Dataset AOI Available PS Scenes Scenes Intersecting with OSM Mask for Railway Vehicle A PS scene intersecting with OSM Mask chipped into grids Final Chips used for annotation Data Collection Funnel
- 20. Aircraft WatercraftBuilding Miscellaneous Small Aircraft Passenger Aircraft Small Boat Maritime Vessel Cargo Ship Residential Hotel Restaurant Office Worship Silo StorageTank Railway Vehicle Swimming Pool Parking Lot Baseball Field Shipyard Shipping Container Vehicle Disasters Ontology (SS)
- 21. Aircraft WatercraftBuilding Miscellaneous Small Boat Maritime Vessel ParkingLot Shipyard Disasters Ontology (PS)
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- 31. Performance Characterization overdifferent sizes
- 32. PR Curve forModels with different thresholds
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- 36. Vessel_name: BIRITE OLDENDORFF Callsign:CQIC5 Vessel_type: Cargo Flag_country: Portugal Destination: Oakland Heavy Traffic Light Traffic Telemetry / Cellphone Data
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- 38. South Passage, Australia– October 1, 2015 Thank You! Reach out at gopal@planet.com Open Positions: Machine Learning Engineer Software Engineer, Machine Learning Infrastructure Full Stack Geospatial Engineer, Analytics Services www.developers.planet.com | API Documentation www.planet.com/markets/education-and-research | Research Projects
- 39. Planet Stories Blog SpatiotemporalAnalytics on Geospatial Imagery Planet Developer Center and Data API Education and Research using Planet Imagery Additional Resources Proprietary and Confidential - not to be distributed