Can We Trust AI for Sat Imagery Processing
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Interconnectivity Panel – EU Space Week 2018 “Can we trust AI for Sat imagery processing?” by Taras Matselyukh, OPT/NET BV
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Can We Trust AI for Sat Imagery Processing
- 1. Taras Matselyukh, CEO and founder of OPT/NET BV Bridging the gap Between Technology & Humans Can we trust AI for EOD processing? In collaboration with ESA and Copernicus Data Hub
- 2. Seva M. Business Dev. Director past: BD/Account Manager Twona/Pharmaceutical Design Mohan R. Marketing Director Co-Founder: Core Digital Strategy, Kane & Rao Group Taras M. CEO/Director 20 year+ industry Experience past: Cisco/Juniper Erik D. CCO past: CEO Stack State VP Sales EMEA Digital Guardian Gianluca V. CFO Co-Founder: Equidam Founder: Metrics Specialist Vlad Z. Software Dev. Team Lead Backend systems Big-data visualisation Business Acceleration Partners OPT/NET BV TEAM
- 3. ACCOLADES Innovation Summit Winner EC Security Challenge 2017 Winner, 2018 Finalist Hello Tomorrow Top 500 Globally Accenture Innovation Awards Top 25 - Living & Working Business and Technology Partners The AIconics awards, 2018 San Francisco AI Summit Best Innovation in RPA, shortlist
- 4. just a clever way of doing linear algebra?Super power that can destroy us all? or WHAT IS AI?
- 5. MOST POPULAR TYPES OF AI FOR EOD PROCESSING ML Supervised Unsupervis ed Structured environmen ts Needs labeled data Unstructure d environmen ts Needs good model
- 9. COMPUTER VISION WITH AI
- 10. Massive Volume of Data FLOODS IN VIETNAM
- 12. OPTOSS AI Approach Grouped Alarms LOGS & KPIs Feature extractionAlarm detection & feature engineering Supervised Learning ML & Clustering Classification
- 13. Satellite collects imagery (SAR, SLSTR, etc.) AI Engine analyses SAR data in real time AI Engine autonomously clusters & alarms Operators in development AI Engine actually detected the ‘eye of the storm’ footprint on the ground and sea as we monitored the Florence Hurricane on Sept 14 2018 while it hit East cost of US
- 14. OPTOSS AI GEO SOLUTION 100K developers from all over the world 2500+ applications developed Semifinalist - among top 10 solutions Concept Architecture for Flood Detection Team OptOSS AI GEO Architecture for Flood Detection Team OptOSS AI GEO Copernicus open data portals Operator selects imagery and pre-processes the SAT data, then pushes the Docker image into IBM Cloud for processing docker containers are executed in IBM Cloud on demand AI detects flooded areas and storm footprints processed maps are stored in COS as COGTIF Web app serves the data to the users of the service Users of the service monitor the flooded areas in the web browser
- 15. OptOSS AI Satellite Observation GUI COPERNICUS SECURITY CHALLENGE WINNER Ivan Konaktchiev, Policy Officer European Commission The jury selected OPTOSS as the winner of this challenge because it deals with large amounts of observational data under stressful conditions. “ ”
- 16. CONTACT @OptNetConsBV +31 (0) 72 581 5940 taras@opt-net.eu Managing the chaos since February 2018 OPT/NET BV http://www.opt-net.eu
- 17. HOW DOES IT WORK? 1.Data Streamed into OptOSS AI 2.Real time Analysis 3.Autonomou s Clustering 4.Educatio n by Analyst 5.Appropriat e Response 6.Knowled ge Recycle
Editor's Notes
- Good afternoon. I am Taras Matselyukh, CEO and founder of OPT/NET ID 180886: TSAR AI - rapid and precise surface change detection
- Our team is young but experienced with proven track record. OPT/NET is working with potential users and user-clients already and expanding our service portfolio: *We have an expressed interest from EY Innovation department to jointly develop new solutions in Security market segment.
- During the 2018 World Government Summit in Dubai LAST week (Feb 13), Google X co-founder Sebastian Thurn stated that artificial intelligence innovations would usher in an era of “superhuman” workers who are capable of doing far more work than what is conventionally possible. OPTOSS AI is doing it NOW! We give engineers of telecoms “super human” powers!
- Collecting and possessing data alone is not enough … the real challenge is to assess risks, predict affected areas and detect damage in very short time, as close to real-time preferably. This task is similar to finding a proverbial needle in a haystack. Limited time to response makes this task impossibly difficult.
- Can human operators solve this challenge?… As the disaster threat continuously increases along with the sheer volume of EOD, the human capability to deal with the challenge remains the same. It takes time, money, and effort to train satellite imagery analysts, but historical performance illustrates that costly and tragic mistakes are still being made by authorities when alerting populations about local disasters and emergencies[3]. These mistakes are root causes in more than 60% of all incidents. Short time to make the right decisions multiplies the effects of wrong choices - which lead to losses. Our conclusion is that current practices are insufficient for timely reaction and prevention of such situations.
- Our response to the problem is an automatic AI assisted disaster monitoring and alerting system.
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