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Theia H4D Stanford 2018

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business model, customer development, hacking for defense, H4D, lean launchpad, lean startup, stanford, startup, steve blank, pete newell, bmnt, video

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Theia H4D Stanford 2018

  1. Identifying objects in U2 high-resolution imagery Team Theia Minjia Zhong MCS / CISAC Christopher Yeh M.S. CS (AI track) David Kingbo MSx 18 (Consulting) Joseph Lee M.S. MS&E / AI Original Problem Statement Develop the capability for analysts to automate object identification in order to focus on the highest priority geographic areas most relevant to their task at hand. Final Problem Statement Develop machine learning software for imagery analysts to detect anomalous activity in aerial and satellite images in order to prioritize relevant geographic areas for further analysis. 100 Interviews US Air Force, 9th Intelligence Squadron Sponsor
  2. Know Your Customer Problem Finding Pivot 1 Week 1 Week 3 Week 5 Week 7 Week 9 Pivot 2 The Evolution of Our Hypotheses/MVPs Go Deep Product Development Week 2 Week 4 Week 6 Week 8 Week 10 Get Early Wins Deployment
  3. Know Your Customer Problem Finding Week 1 Week 3 Week 5 Week 7 Week 9 Week 2 Week 4 Week 6 Week 8 Week 10 “Manual labor is the biggest problem. … Sometimes we have to turn down a request, but then [our clients] won’t come back.” Imagery Analyst at the 9th Intelligence Squadron The Evolution of Our Hypotheses/MVPs Go Deep Product Development Get Early Wins Deployment Pivot 1 Pivot 2
  4. Why is automation relevant to the film camera? Why does it matter to improve film camera processes? Is the film camera even necessary? Is there demand for their products? What is the mission of the film camera and how do they serve customers? Know Your Customer (Week 1-2)
  5. Initial Mission Model Canvas (Week 1) Value Proposition Increase detection efficiency Reduce operational overhead Beneficiaries 9th IS DoD/NGA Satellite companies
  6. Getting Out of the Building (Week 3)
  7. Mapping Out the 9th IS Imagery Analysis Workflow (Week 1-3) Image Collection (9th IS) ❏ How does the 9th IS work? ❏ How does it serve its customers? Image Analysis (IC) Dissemination Use
  8. Identifying Bottlenecks in 9th IS Workflow (Week 1-3) Image Collection (9th IS) ❏ How does the 9th IS work? ❏ How does it serve its customers? Image Analysis (IC) Dissemination Use
  9. Identifying Bottlenecks in 9th IS Workflow (Week 1-3) ● Inefficient manual film processing ● Time consuming manual imagery analysis ● Lack of awareness of film camera capabilities
  10. Know Your Customer Problem Finding “I don’t care how many cars or trucks. I care about what sorts of activities are going on.” NGA Manager Week 1 Week 3 Week 5 Week 7 Week 9 Week 2 Week 4 Week 6 Week 8 Week 10 The Evolution of Our Hypotheses/MVPs Go Deep Product Development Get Early Wins Deployment “Manual labor is the biggest problem. … Sometimes we have to turn down a request, but then [our clients] won’t come back.” Imagery Analyst at the 9th Intelligence Squadron Pivot 1 Pivot 2
  11. Hypothesis Speeding up the film image developing and scanning process would enable the 9th IS produce more relevant intelligence. Feedback “For me it doesn’t matter if [the film scanning and digitization process] takes 30 days or 10 days” - Intelligence officer Way Too Many MVPs… (Week 4-5) Film developer with built-in digital scanner and object identification
  12. Way Too Many MVPs… (Week 4-5) Hypothesis An automated object identification software would mitigate imagery underutilization. Feedback “DigitalGlobe works pretty well, but as computer vision gets better, it needs to marry with how an analyst does its job.” - NGA manager Automated Object Identification Software
  13. Way Too Many MVPs… (Week 4-5) Hypothesis Improving imagery search at the NGA would help analysts find relevant images faster, including images from the 9th IS. Feedback “I don’t use the NGA search engine. Talking to people is the best way to find intel.” - Intel Analyst NGA Google Search Engine
  14. Three Layers of Confusion??? (Week 6) National Geospatial Intelligence Agency (NGA) vs 9th IS Beale Air Force Base BENEFICIARY? vs Automated Object Identification NGA Search Engine PROBLEM? Off the Shelf Solution vs New Product PRODUCT?
  15. Who’s Your Beneficiary? (Week 6) vs 9th IS Beale Air Force Base National Geospatial Intelligence Agency (NGA)
  16. Pivot 1: Broadening Our Scope (Week 6) Sponsor 9th IS w/ film images taken by the OBC (optical bar camera) All beneficiaries of AI-based tools ● all aerial image providers, including 9th IS (sponsor) ● military analysts ● intelligence analysts ● mission planners We believe we can have broader impact on aerial imagery intelligence.
  17. A Common Thread (Week 7) A Recurring Need for Automated Image Analysis Potential Beneficiaries Validating Feedback USAF Intel Analyst “Change detection is a big deal for analysts” Google Imagery Analyst “I think you all have an interesting idea and even with competition there is really a lot of room in this nascent industry.” DIUx Project Manager “There is a huge need for innovation in this space, only 2 key players right now - Orbital Insights + Progeny” NGA Manager “It’s really about object identification and being able to search through that” USAF Lt. Col. “The issue is that this is hoovering up a lot of data and the analysts cannot process all of it… This is why I need AI.”
  18. Getting Validation (Week 8) A Recurring Need for Automated Image Analysis Continuous Checking: “One of the top tech needs for NGA is Change Detection Alert Service, specifically from multiple sources. Being able to layer different data together, and the AI would say’“Hey, something’s changed here!’” - Senior Officer at NGA Image adopted from a screenshot taken from Spaceknow (https://www.spaceknow.com/)
  19. Know Your Customer Problem Finding “I can dedicate 15 analysts for 2 to 3 hours a week [to label and curate an imagery dataset]” Officer at 9th Intelligence Squadron Week 1 Week 3 Week 5 Week 7 Week 9 Week 2 Week 4 Week 6 Week 8 Week 10 The Evolution of Our Hypotheses/MVPs Go Deep Product Development Get Early Wins Deployment “Manual labor is the biggest problem. … Sometimes we have to turn down a request, but then [our clients] won’t come back.” Imagery Analyst at the 9th Intelligence Squadron “I don’t care how many cars or trucks. I care about what sorts of activities are going on.” NGA Manager Pivot 1 Pivot 2
  20. Mission Model Canvas (Week 9) Key Partners 9th Intelligence Squadron Key Activities Training a Machine Learning Model to detect meaningful activity Key Resources Labels of meaningful activity in images relevant to the Intelligence Community
  21. Pivot 2: Leveraging Our Partnership with the 9th Intelligence Squadron (Week 9) “What I can possibly dedicate is 15 analysts [labelling and curating images] for 2 to 3 hours a week... If this is something I can get approved from my squadron leader, we can get 150 analysts.” - Officer at 9th Intelligence Squadron9th IS Beale Air Force Base
  22. Iterating on our MVP (Week 9) Allowing for new datasets: “There are likely to be some future use cases that we might not even know now.” - Officer at the 9th Intelligence Squadron
  23. Iterating on our MVP (Week 9) Allowing for new datasets: Analysts can then manually label the images that correspond to the type of activity that they were interested in searching for.
  24. Where We Are Now
  25. Our Current MVP Change Detection “Definitely something like this is something I hope we have going forward in the future.” - Image Analyst at 9th IS Image adopted from a screenshot taken from Spaceknow (https://www.spaceknow.com/)
  26. Our Current MVP Integrating Insights / Other Reports to pull up “Making sure that we pull from different intelligent services... If we can pull everything on one user- friendly interface.” - Image Analyst at 9th IS Image adopted from a screenshot taken from Spaceknow (https://www.spaceknow.com/)
  27. Funding Strategy Multiple funding opportunities We can look at several funding opportunities in parallel to build our product. SBIR, In-Q-Tel and DIUx are the most promising funding sources available for our MVP.
  28. What’s Next: Handoff Theia
  29. Acknowledgements Our work would not have been possible without our sponsors at the US Air Force 9th IS (especially CMSgt Ian Eishen, Tsgt Nathaniel Maidel, SMSgt Lisa Payne, and Capt. Timothy Wilde), H4D military liaisons, our mentors (Samir Patel and Michael Chai), countless military and commercial partners, and the teaching staff and TAs (especially Will Papper).

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