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GEOG 885X - Advanced Analytic Methods in Geospatial Intelligence

This is a sample syllabus.

This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.

Overview

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.

Objectives

Students who excel in this course are able to:

  • LO-1: Apply the geospatial intelligence process including problem spatialization, recording, discovering, tracking, comprehending, and communicating analytic results.
  • LO-2: Contrast the strengths and limitations of the human and machine in geospatial analysis.
  • LO-3: Explain the professional and ethical considerations surrounding machine-driven analysis, automation, and GeoAI in geospatial intelligence analysis.
  • LO-4: Elaborate about the application of human cognitive techniques (Structured Analytic Techniques), computational thinking, GeoAI and automation in geospatial analysis.
  • LO-5: Compare the potential impact of human-machine collaboration on decision-making across different applications.
  • LO-6: Apply critical thinking and problem-solving skills to analyze complex geospatial intelligence problems using a human-machine collaborative approach.
  • LO-7: Defend the results of a geospatial analysis to decision-makers while safeguarding trust, credibility, and accuracy of analytic insights.
  • LO-8: Articulate an understanding of emerging trends and future directions in human-machine collaboration for geospatial intelligence analysis.

Required Materials

Prerequisites

None.

Expectations

We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 12 – 15 hours per week on class work.

Major Assignments

Grading

Course Schedule

Schedule