George Mason University
Establishing Pattern-of-Life from Rapid Revisit Satellite Imagery using Machine Learning
In this talk, I will describe the machine learning system we have built at BlackSky to process, analyze, and enrich the tremendous volume of data we capture from our satellites every day. The last decade has seen an explosion in the availability and affordability of commercial satellite imagery. This growth has yielded tremendous improvements in our ability to perform environmental monitoring, commercial development, and defense and intelligence planning. Recently, BlackSky has launched the first of its Globals constellation of imaging small sats. With revisit rates of up to eight times a day, this constellation will offer temporal resolution unattainable by current commercial satellite imaging solutions. While this level of insight enables many critical new use cases, the deluge of data has made it difficult for imagery analysts to process all the data and prioritize their efforts. This presents an immediate need for novel machine learning and computer vision techniques which can identify and flag significant changes among thousands of images per day. Through the use of deep learning, we are able to extract objects from scenes and establish temporal baselines of activity at facilities. By continually monitoring these facilities, we can establish pattern-of-life and, importantly, any deviations from the established patterns.
|Date||April 10, 2019|
|Start/End Time||11:00 AM - 12:00 PM|
|Location||Building 3 (Goett) Auditorium|
|Contact||Dr. Carlos Cruz|
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