Topic/ Summary: 

Applying Smart Cameras and AI for Asset Management

About the presentation: This presentation will include an overview of emerging applications of vehicle based smart cameras and artificial intelligence for automated data collection, inspections and condition assessments of various assets, including road infrastructure, transit infrastructure, and other related assets. The presentation will include an overview of the underlying AI technology, examples of the data which is being collected and how it can be applied towards asset management, overview of different use cases, and key performance indicators reported by organizations which have used the technology. Attendees of the presentation will: (1) learn about possible use cases of vision based artificial intelligence in asset management applications; (2) understand what data should be collected, and what data can be generated using these technologies; and (3) gain insights as to how these can be used to supplement existing processes and best practices.

About the speaker: Roy Tal is an accomplished professional with more than twenty years of experience in engineering, technology, operations, and management. Roy’s work involves strategic direction, partnerships, projects, and investments in artificial intelligence and smart city technologies, and is a named inventor on numerous patents in the field. Roy enjoys collaborating with professionals in the public sector on developing and testing new approaches to apply AI and digital transformation, and his work has supported CITYROVER and its municipal partners in collectively achieving over 30 innovation awards to date. Roy holds a Bachelor in Computer Engineering from the University of Western Ontario and is a licensed Professional Engineer.

Agenda:

• National & Local Branch Updates
• Applying Smart Cameras & AI for Asset Management: Presentation by Roy Tal, P.E.
• Open Discussion

Call for speakers/volunteers

Microsoft Teams meeting

Join: https://teams.microsoft.com/meet/38886311227133?p=SY66FuMWHcUHepBnhM

Meeting ID: 388 863 112 271 33

Passcode: 3HX2fj9k