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Lights Lights Pr oduct ion & Consumpt ion TEAM USS ENTERPR - PowerPoint PPT Presentation

Lights Lights Pr oduct ion & Consumpt ion TEAM USS ENTERPR TEAM USS ENTERPR 1.2 1.2billion billion People do not waste light 2 TEAM USS ENTERPR TEAM USS ENTERPR [Source]: World Economic Forum 6.5 6.5 billion billion Contribute


  1. Lights Lights Pr oduct ion & Consumpt ion TEAM USS ENTERPR TEAM USS ENTERPR

  2. 1.2 1.2billion billion People do not waste light 2 TEAM USS ENTERPR TEAM USS ENTERPR [Source]: World Economic Forum

  3. 6.5 6.5 billion billion Contribute to excessive usage of light 3 TEAM USS ENTERPR TEAM USS ENTERPR [Source]: World Economic Forum

  4. $3.3 $3.3 billion billion Cost of wasted light in U.S yearly 21 21 million tons of co2 million tons of co2 Amount of wasted light yearly in U.S is equivalent to 875 875 million trees million trees Required to offset CO2 4 TEAM USS ENTERPR TEAM USS ENTERPR [Source]: U.S. Department of Energy

  5. Where is the waste from? Where is the waste from? 5

  6. Compr ehensive M odel of t he Wor l d Day Time Space Imagery Night Time Space Imagery Human Traffic Data Synthetic Aperture Radar 6 TEAM USS ENTERPR TEAM USS ENTERPR

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  9. Plan of Action Plan of Action 1) Gamify identification of inefficient light sources 2) Educate the public on responsible use of light 3) Training of Machine Learning Algorithm to identify light sources 4) Implementation on ground based solutions 9 TEAM USS ENTERPR TEAM USS ENTERPR

  10. Customer Base Customer Base Our key customers would be the local Urban Planners and OEMs of Smart Infrastructure solutions 1) [Local] 110,000 Lamp Posts in Singapore 2) [Regional] 26 ASEAN Cities of the Smart Cities Network 3) [Global] 1,000 Smart Cities Projects Worldwide 1 0 TEAM USS ENTERPR TEAM USS ENTERPR [Source]: CNA, Centre for Liveable Cities Singapore, The Business Times

  11. Routemap Routemap Potential Partner Cities Potential Partner Cities Artificial Artificial Game Game • Reduction of light Reduction of light Intelligence Intelligence Development Development • Job creation Job creation (Machine Learning) (Machine Learning) • Revenue Revenue 2 months 2 months 4 months 4 months 6 months 6 months 1 1 TEAM USS ENTERPR TEAM USS ENTERPR

  12. Let’s go light lite Thanks! Thanks! ANY QUESTIONS? ANY QUESTIONS? 1 2

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