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IMED 2018: An intro to Remote Sensing and Machine Learning

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Hamed Alemohammad, Ph.D., Lead Geospatial Data Scientist, Radiant Earth Foundation: An intro to Remote Sensing and Machine Learning.

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IMED 2018: An intro to Remote Sensing and Machine Learning

  1. 1. An Intro to Remote Sensing and Machine Learning HAMED ALEMOHAMMAD LEAD GEOSPATIAL DATA SCIENTIST, RADIANT EARTH FOUNDATION IMED, 2018, Vienna, Austria
  2. 2. Remote Sensing Measurement of a quantity associated with an object by a device not in direct contact with the object
  3. 3. Satellite Remote Sensing Satellites carry instruments or sensors which measure electromagnetic radiation coming from the earth- atmosphere system. 3
  4. 4. Measuring Earth Surface and Atmospheric Properties  The intensity of reflected and emitted radiation to space is influenced by the surface and atmospheric conditions.  Thus, satellite measurements contain information about the surface and atmospheric conditions.
  5. 5. Electromagnetic Radiation Earth-Ocean-Land-Atmosphere System: • Reflects solar radiation back to space • Emits Infrared and Microwave radiation to space
  6. 6. Interaction with Vegetation Example: Healthy, green vegetation absorbs Blue and Red wavelengths and reflects Green and Infrared. Since we cannot see infrared radiation, we see healthy vegetation as green.
  7. 7. Spectral Signatures in Imagery Remotely sensed imagery acquires information in different wavelengths, representing different parts of the Electromagnetic Spectrum.
  8. 8. Vegetation Indices
  9. 9. Solar Induced Fluorescence (SIF)  Energy absorbed by plant through its chlorophyll used for gross primary production (GPP) lost as heat re-emitted (SIF: byproduct)  SIF responds to stressors (water, light, T). Babani, F., et al. 2005
  10. 10. Except for Indonesia all tropical regions exhibit some seasonal cycle due to light/water limitations
  11. 11. 11
  12. 12. Microwave | Thermal | Infrared |Visible| Visible-NIR Vegetation Index Solar Induced Fluorescence IR Thermal Radar Backscatters Passive-Microwave Optical Depth NDVI, EVI, … Photosynthesis Canopy Temp. & Evapotranspiration Top-Canopy Biomass Canopy-column Water Content
  13. 13. Mean Annual Soil Moisture
  14. 14. Mean Annual Precipitation
  15. 15. Nitrogen Dioxide from Sentinel-5P Satellite credit: ESA
  16. 16. Nitrogen Dioxide Mapping credit: Google
  17. 17. Satellite vs Sensors
  18. 18. Spatial Resolution Actual size of each pixel of the image
  19. 19. Spatial Resolution vs Extent Generally, the higher the spatial resolution the less area is covered by a single image.
  20. 20. The European Copernicus Initiative
  21. 21. Atmospheric Transparency Average cloudiness (2002 - 2015) NASA Earth Observatory
  22. 22. Radar Measurements across Pivotal Agricultural Systems Google Earth
  23. 23. Credit: Jörgen Eriksson Artificial Intelligence (AI) is about bringing together computers and humans in ways that enhance human life.
  24. 24. Intelligence Augmentation (IA): Computation and data used to create services that augment human intelligence and creativity.  Search engine  Natural language translation Intelligent Infrastructure (II): A web of computation, data and physical entities that makes human environments more supportive, interesting and safe.  Starting to appear in domains such as transportation, medicine, commerce and finance. Credit: Michael Jordan, Professor at UC Berkeley
  25. 25. Computer Computer Data Program Output Data Program Output Traditional Programming Machine Learning
  26. 26. source: COGNUB
  27. 27. Caution
  28. 28. Random Forest
  29. 29. Neural Networks
  30. 30. Deep Learning SegNet architecture
  31. 31. Rural schools in Liberia Courtesy of Zhuangfang Yi Development Seed
  32. 32. credit: Space-Net
  33. 33. Road Tracer Credit: MIT CSAIL
  34. 34. Crop Classification Credit: Rose M. Rustowicz
  35. 35. Training Data Challenges  Capturing the wide range of possible outcomes both in space and time;  Accuracy;  GeoDiversity  Accessibility;  Inter-Operability;  ML-Readiness;
  36. 36. Open source machine learning commons for Earth Observations. Promoting creation of open libraries of labeled images and algorithms to advance ML for global development, and democratize ML applications for EO data. Developers can join the collaborative initiative and contribute their tools and knowledge on Github. Imagery training data will be created as STAC compliant and in COG format.
  37. 37. • The Problem: Need for an open, dynamic, global, and comprehensive LC map Open Training Library for Land Cover Classification: • Using Deep Learning for labeling imagery • Crowdsourcing and citizen science to verify / correct the labels Sponsored by: Open Source 10 m resolution Global ML Centric • Solution: Training labeled image library for land cover classification
  38. 38. Radiant Earth Foundation: Vision & Mission  Open Geospatial Data for Positive Global Impact  Connecting people globally to Earth Imagery, geospatial data, tools and knowledge to meet the world’s most critical challenges
  39. 39. What we do Provide Open Access to Earth Imagery & Tools Provide Education on Geospatial Data & Tools Provide Neutral Leadership to Enhance Industry-Wide Collaboration
  40. 40. Attributes of the Platform AGILE Experiment with data, visualization, and collaborate in a cross-domain multidisciplinary ecosystem. OPEN Work with open imagery, data sets and technology standards. NEUTRAL Discover both government & commercial imagery, and collaborate with tech-and non- technical users at the intersection of global development & remote sensing. COLLABORATIVE Learn and share ideas to improve collaboration across domains. FEDERATED Find and work with diverse imagery data sets covering the globe with a federated catalogue.
  41. 41. Available Open Imagery Datasource Temporal Coverage Temporal Revisit Spatial Resolution Sentinel 2-A/B 2015 - present 5 days 10 m Landsat 4/5/7/8 1982 - present 16 days 30 m MODIS 2000 - present 8 day composite from daily 250 m ISERV 2012 - 2015 Specific operation times 3.5 m
  42. 42. Platform Features  Supporting any imagery type:  Satellite  Drone  Airborne  Uploading pipelines:  Local  Dropbox  Amazon Web Services (AWS) S3 Bucket  Planet API Connection  Radiant Earth Foundation API
  43. 43. Platform Interfaces app.radiant.earth doc.radiant.earth
  44. 44. Radiant APIs Raster APIs api.radiant.earth/platform Imagery from Drones, Aerial, Balloons, Satellites Projects Area of Interests Annotations Band math algos in Labs Sharing via OGC (e.g. WMTS, etc) Teams, Organizations Data APIs api.radiant.earth/{endpoint} Weather forecasts / weather Air Quality / air-quality Population / population Crop Suitability / crop-suitability Satellites / satellites
  45. 45. Platform Demonstration
  46. 46. Get in touch Follow Us 740 15th St NW, Suite 900 Washington DC 20005 + 1. 202.596.3603 hello@radiant.earth www.radiant.earth | app.radiant.earth | help.radiant.earth | demos.radiant.earth @OurRadiantEarth https://www.facebook.com/OurRadiantEarth Q & A