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GEOG 885 explores the challenges and opportunities created by combining human expertise with computational analysis methods in the field of geospatial intelligence (GEOINT). The course focuses on the science and technology of human-machine collaboration using geospatial artificial intelligence (GeoAI) in GEOINT and the professional and ethical concerns that must be considered as we move forward in this rapidly evolving field. Students completing this course will be able to explain and apply Structured Analytic Techniques (SATs), automation methods, and GeoAI tools in combination to solve geospatial intelligence problems. Students will create analysis workflows that ensure the efficiency, credibility, and accuracy of analytical insights. SATs are evaluated by students to gauge their ability to improve the quality and rigor of analysis. Students will also learn how to apply emerging GeoAI tools to summarize data and perform analytical tasks that have typically required human intelligence. GEOINT plays an increasingly critical role in supporting decision-making across a broad range of industries, from defense and intelligence to environmental monitoring and urban planning. The amount of geospatial data available today is overwhelming, but by leveraging the strengths of both humans and machines, we can gain deeper insights into high-dimensional spatial data and more effectively solve geographic problems. The course does not require any technical background, and it is open to students from all disciplines.
Video: Introduction to GEOG 885 (1:53 minutes)
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TODD BACASTOW: So early on when we developed this course, we focused primarily on structured analytic techniques, which are human in nature. Since that, a lot of things have changed. Probably the primary one are automation, augmentation in geo-AI, those three components.
LEANNE SULEWSKI: And so, this course has transformed to take into account not just those structured analytic techniques, which we still pay homage to in this course, but also bring in that automation, augmentation, and GEO-AI perspective that the machines bring, and that true human-machine collaboration that creates effective methodologies that can truly answer geo-one's hard question.
TODD BACASTOW: One of the things that I find really helpful in trying to tackle this problem, and for people to learn and appreciate it, how to handle it, was breaking down GEOINT into six threads that intertwined together. But breaking it down in those components and addressing each one of those allows us to effectively look at it. So, what do you think the most important thing that a student should walk away with or will walk away with from this course?
LEANNE SULEWSKI: A student taking this class should expect to walk away with, and what we want them to walk away with is this renewed appreciation for human-machine collaboration to understand the importance of that collaboration, that it's truly working together. It's not just one or the other. And using the six threads that we give them to really weave together a holistic methodology that provides the right and careful mixture of human-machine collaboration for their problem at the time. Would you agree?
TODD BACASTOW: I agree.
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Want to join us? Students who register for this Penn State course gain access to assignments and instructor feedback and earn academic credit. For more information, visit Penn State's Online Geospatial Education Program website. Official course descriptions and curricular details can be reviewed in the University Bulletin.