Tensorflight - Actionable insights from aerial imagery
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Zbigniew Wojna, Founder, Tensorflight presenting at Project Juno's second Machine Intelligence Showcase.
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Tensorflight - Actionable insights from aerial imagery
- 1. Actionable insights from aerial imagery
- 2. Agenda TensorFlight demo How does our software work? How can we tailor our software to your needs? Who are we and who are our clients? Market analysis
- 12. Agenda TensorFlight demo How does our software work? How can we tailor our software to your needs? Who are we and who are our clients? Market analysis
- 13. TensorFlight - team and current users Team Users Drone service partners: Direct users: Founders Advisors Other Subcontractors - map annotators ● Training data set preparation Robert Kozikowski ● Ex-Google and Facebook engineer ● Big data and scaling Zbigniew Wojna ● Deep learning Ph.D. at UCL (World records) ● Google DeepMind, Brain Street view, Microsoft, Nvidia Jan Malaszkiewicz ● Strategy consultant, Boston Consulting Group Deep learning experts ● Pieter Abbeel, professor at UC Berkley, OpenAI ● Kyunghyun Cho, professor at NYU ● Marcin Moczulski Ph.D. More than 1000 users on DroneDeploy
- 14. Source: https://blogs.nvidia.com/wp-content/uploads/2014/09/InsideImagenet.png Zbigniew - The best model on Image Classification ImageNet 2015
- 15. Zbigniew - The best model on MS COCO detection challenge 2016 Source: https://adriancolyer.files.wordpress.com/2016/04/imagenet-fig4l.png?w=1132
- 16. Agenda TensorFlight demo How does our software work? How can we tailor our software to your needs? Who are we and who are our clients? Market analysis
- 17. • DJI drones via DroneDeploy auto pilot app • Your own aerial imagery - aircraft, drone or satellite • Terravion • Orthonormalization and tiling via DroneDeploy or OpenDroneMap • Request processing directly from TensorFlight or via DroneDeploy app TensorFlight specializes in Computer Vision Image analysisMaps formattingData collection • Accurate - State of the art computer vision via deep learning • Quick - Scalable, distributed deployment in the cloud • Usable - Analyze results in depth on web frontend TensorFlight
- 18. We are expanding our Computer Vision capabilities Available today: • Trees: healthy, unhealthy, dead • Livestock • Cars • Parking spots • Houses • Crops • People Development in progress: • Trees – recognition of tree type through crowdsourcing our users • Recognition from plane imagery • Area estimation of plantations • Area estimation of rooftops • Area estimation of damaged rooftops
- 19. Showcase - Successful app on DroneDeploy platform TensorFlight • Dead / Healthy tree counter • Histogram of tree sizes • More than 200 000 acres of land mapped in our database
- 20. 1000+ users across the World
- 21. Agenda TensorFlight demo How does our software work? How can we tailor our software to your needs? Who are we and who are our clients? Market analysis
- 22. Step 1: Gathering maps Step 2: Human annotation of examples to create a training data set (10K-100K examples needed) – we have our own subcontractors • Image detection - rectangle around each object - 1 month ETA • Image segmentation - detailed line around object and the volume - 3 months ETA Step 3: Using the training data set to adapt our algorithm Step 4: Processing the production data set Our algorithm can be adapted to other object categories Algorithms achieve human level accuracy on the majority of computer vision tasks
- 23. How to map an area? Small/Medium Big Huge • Purchase a DJI drone • Take pictures, convert to map format • Find a company operating a fleet of drones • Payment method: price per acre • Example: find a provider on DroneDeploy Mapping Directory • Use Terravion • Dependent on needed level of detail • Consider satellite imagery (price effective, but potentially not enough detail): Planet Labs
- 24. Agenda TensorFlight demo How does our software work? How can we tailor our software to your needs? Who are we and who are our clients? Market analysis
- 25. Reference point – total market size estimates2020 addressable market Conservative estimate for orchard-related product market is $90m in 2020 OtherInsurance 7 TotalAgriculture World total permanent crops (orchards, vineyards) land area: ~470m acres1 Innovation adoption rate follows typical Bass diffusion model2 , conservative scenario: each acre covered only once 100 0 400 500 300 200 90 Cumulative coverage of permanent crops (m acres) Most applicable estimate for drone based solutions market size ambition: what is the market value of services that can be replaced by drones by 2020? 1. Source: Worldbank; 2. Standard forecast for adoption rate of a new technology superior to current products; 3. Source: PwC “Clarity from above” report $B Conservative scenario: we only play in Insurance area ($7B), not counting Agriculture market Total addressable market by 2020: $90m, assuming: $1 drone plant-counting related revenue per acre Worldwide sales access and wide coverage of plant recognition models This is a very conservative estimate – only for permanent crops, doesn’t consider multiple usage on the same area
- 26. Q&A