"Using Satellites to Extract Insights on the Ground," a Presentation from Orbital Insight
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Orbital Insight, founded in 2013, leverages satellite imagery and artificial intelligence to extract socio-economic insights globally for asset management firms and government agencies. Their technology combines deep learning and computer vision to analyze data from various satellite providers, focusing on measuring parameters like retail activity and crude oil inventory. The document discusses the challenges and future possibilities for satellite data applications in areas such as urban development, agriculture, and industrial monitoring.
"Using Satellites to Extract Insights on the Ground," a Presentation from Orbital Insight
- 1. Copyright © 2017 Orbital Insight 1 Using Satellites to Extract Insights on the Ground Dr. Boris Babenko, Orbital Insight May 2017
- 2. Copyright © 2017 Orbital Insight 2 • Founded in 2013 • Our mission: to understand socio-economic trends on a global scale, using aerial imagery • Our technology: deep learning and computer vision + data science • Our partners: Airbus, DigitalGlobe, Planet, ImageSat, MDA Corporation, Urthecast, USGS Landsat • Our customers: >70 asset management firms, several U.S. government agencies, 2 global nonprofits About Orbital Insight
- 3. Copyright © 2017 Orbital Insight 3 Background: Why Space Now?
- 4. Copyright © 2017 Orbital Insight 4 • Building and launching a satellite is cheaper than ever before • Artificial intelligence has made great advances • Satellite imagery: a previously untapped resource outside of government The Perfect Combination Image Credit: NASA
- 5. Copyright © 2017 Orbital Insight 5 • In a few years, you’d need every person in New York City to spend all day, every day, looking at photos in order to have humans lay eyes on each satellite image being generated daily. Why Do We Need Artificial Intelligence? Image Credit: matheuslotero, Flickr, CC Attribution license
- 6. Copyright © 2017 Orbital Insight 6 Satellites: Not One-Size-Fits-All
- 7. Copyright © 2017 Orbital Insight 7 • Bigger satellites • More expensive to make & launch (~5,000 lb), so fewer in orbit • As a result, less frequent imagery • Higher resolution photos (e.g., 0.5 m) • Smaller satellites (nanosats, cubesats, etc.) • Less expensive to make & launch (~10 lb), so more in orbit • As a result, more frequent imagery • Lower resolution photos (e.g., 3 m) Size, Frequency and Detail Photo Credit: DigitalGlobe Photo Credit: Planet
- 8. Copyright © 2017 Orbital Insight 8 What Can We Measure?
- 9. Copyright © 2017 Orbital Insight 9 • Convolutional neural network trained to detect cars in 0.5 m imagery – cars are only a few pixels in size • Track hundreds of retail chains, malls, etc. • Parking lot traffic correlates with sales Retail Sales Forecasting Photo Credit: Orbital Insight/satellite imagery: DigitalGlobe
- 10. Copyright © 2017 Orbital Insight 10 0 0.05 0.1 0.15 0.2 0.25 0.3 $0 $100 $200 $300 $400 $500 $600 $700 $800 1/1/10 7/1/10 1/1/11 7/1/11 1/1/12 7/1/12 1/1/13 7/1/13 1/1/14 7/1/14 1/1/15 7/1/15 1/1/16 7/1/16 CMGIndexedTraffic CMGStockPrice CMG Stock Price CMG Indexed Traffic Street consensus estimates fail to pickup ongoing slowdown in traffic, stock corrects -40% after six-month dislocation Orbital Insight uncovers a slowdown in traffic, missed by consensus Stock declines -20% after nine-month dislocation from traffic patterns Stock declines -45% to more closely correlate to the 12 month deterioration in traffic Orbital Insight observes traffic peak in early 2015, stock continues to deviate from underlying deterioration in traffic Orbital Insight uncovers deterioration in traffic, dislocation from Wall Street expectations. Helping Uncover Dislocations to Street Expectations
- 11. Copyright © 2017 Orbital Insight 11 -0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 $0 $5 $10 $15 $20 $25 $30 $35 $40 $45 $50 1/1/10 7/1/10 1/1/11 7/1/11 1/1/12 7/1/12 1/1/13 7/1/13 1/1/14 7/1/14 1/1/15 7/1/15 1/1/16 7/1/16 JCPIndexedTraffic JCPStockPrice JCP Stock Price JCP Indexed Traffic JCP stock price performance mirrors traffic after three year period Uncovering Early Short Opportunities
- 12. Copyright © 2017 Orbital Insight 12 What else can be done with a car detector? Nanjing, China • Detect cars across an entire city • Proxy for: urban development, population, income, gasoline demand
- 13. Copyright © 2017 Orbital Insight 13 • Estimating how much oil is in each tank • Tracking 20,000+ tanks worldwide Tracking worldwide crude oil inventory Photo Credit: Orbital Insight/satellite imagery: DigitalGlobe, Planet 0.5 m imagery 3 m imagery
- 14. Copyright © 2017 Orbital Insight 14 Tracking worldwide crude oil inventory
- 15. Copyright © 2017 Orbital Insight 15 Previously known Found by Orbital Insight • Ran CNN across all China to find unknown oil tank farms Building a complete catalog of tanks
- 16. Copyright © 2017 Orbital Insight 16 • Never enough data • Trade-off between spatial resolution and temporal frequency • Clouds • Can’t count cars if they’re covered by clouds • Cloud computing • Computational resources seem infinite and inexpensive… until you start using GPUs • Recruiting engineers (we’re hiring!) Challenges Photo Credit: Orbital Insight/satellite imagery: DigitalGlobe, Planet, USGS
- 17. Copyright © 2017 Orbital Insight 17 Future Possibilities
- 18. Copyright © 2017 Orbital Insight 18 Railcar storage Landuse classification Iron ore stockpiles Aircraft monitoring Heavy industry Photo Credit: Orbital Insight/satellite imagery: DigitalGlobe, Planet, USGS Other applications
- 19. Copyright © 2017 Orbital Insight 19 • More verticals • Infrastructure and asset monitoring • Agriculture • Integrating more data • More satellite imagery • Beyond optical: SAR • Beyond satellites: UAV/Drone imagery • Beyond imagery: AIS, other GIS datasets Where We Go From Here
- 20. Copyright © 2017 Orbital Insight 20 • The Science Behind the Signal: Tracking Unknown Oil Tanks Around the World, Orbital Insight’s blog • From the Macroscope: Home Improvement Stores End 2015 With a Whimper, Orbital Insight’s blog • Leveraging Commercial Applications to Help the World Bank Map Poverty, Orbital Insight’s blog • “Agricultural Crop Health Analysis” in Deep Learning Use Cases for Computer Vision, by Tractica, Embedded Vision Alliance website Resources