Watch "Space Level AI for Monitoring Infrastructure Networks"

Abstract:

Infrastructure networks such as power lines, pipelines, railways, and roadways spread across countries for thousands of kilometers and pass through forests, over various terrain, and cities on their journey to bring energy and other commodities to our homes. During this long journey, infrastructure lines meet with vegetation on many occasions and raise safety, economic, and environmental concerns. Vegetation is the principal reason for outages in power systems and roadway closures that put millions of people in darkness and isolation, with billions of NOK in economic damage each year. News on wildfire sparked by power lines, pipeline leaks, and roadway closures with broken trees are becoming a new normal. In recent years, the dramatic drop in satellite launching cost and the growing number of satellites in orbit significantly reduced the cost of satellite imagery. Commercial satellite providers offer high-resolution images with a daily and sub-daily revisiting frequency for most of the globe. The ongoing satellite data revolution and the advancement in computer vision and AI bring the opportunity to combine scale, frequency, and cost efficiency to enhance vegetation monitoring around massive infrastructure lines. 

This seminar will present some of our research activities and our projects showcase on Space Level AI for Monitoring Infrastructure Networks.

Bio:Prof. Reza Arghandeh is the Director of Connectivity, Information & Intelligence Lab (Ci2Lab.com) and a Full Professor in Data Science and Machine Learning in the Department of Computer Science, Electrical Engineering, and Mathematical Sciences at the Western Norway University of Applied Sciences (HVL), Bergen, Norway. He is also the HVL Data Science Group (HVL.no/ai). Additionally, he is a Research Professor in the Electrical and Computer Department at Florida State University, USA, where he was an assistant professor from 2015 to 2018. Prior to FSU, he was a postdoctoral scholar at the University of California, Berkeley, EECS Dept 2013-2015. He completed his Ph.D. in Electrical Engineering at Virginia Tech. He holds Master’s degrees in Industrial and System Engineering from Virginia Tech (2013) and in Energy Systems from the University of Manchester (2008). His research interests include data analysis and decision support for smart grids and smart cities. His research has been supported by IBM, the U.S. National Science Foundation, the U.S. Department of Energy, the European Space Agency, the European Commission, and the Research Council of Norway.