Graduate Programs in Geospatial Intelligence

The program is offered through the Department of Geographical Sciences in the College of Behavioral and Social Sciences. Geospatial Intelligence provides workforce-focused technical training that gives graduates the skills and expertise to lead new initiatives in the rapidly shifting landscape of GEOINT applications, data collection systems, analytic methods, and mission support. 

Mentoring and advising are an essential part of the program. Students meet with faculty and the academic program director to ensure that educational goals and career learning and development goals are met. Students should contact Professor Ruibo Han via email: ruibo@umd.edu. 

The Graduate Certificate in Geospatial Intelligence has a 12-credit, 4-course curriculum that covers spatial analysis, remote sensing, big data analytics, geo-visualization, programming in web and mobile GIS, and geospatial intelligence in various fields

• Offered in person (major code Z109) or online (major code Z135). The curriculum is identical.
• Provides a well-rounded understanding of the fundamental principles of geospatial intelligence and analysis, including the core theory, methods and protocols for gathering and managing geospatial intelligence data; analyzing and visualizing those data; using the resulting products in operational settings for applied geospatial intelligence; and ensuring the ethical treatment of data and analysis throughout those processes.
• Can be completed in 15 months of continuous part-time enrollment. See Designation of Full-time/Part-time Status.

The plan of study includes two 3-credit core courses (6 credits) and two 3-credit electives (6 credits).

Registration Overview

• See the sample plan of study below. Students should use this as a guide to develop a plan with the academic program director.
• Actual course offerings are determined by the program and may vary semester to semester. Students should note if a course has a pre-requisite or co-requisite.
• Specific class meeting information (days and time) is posted on UMD’s interactive web service, Testudo. Once on that site, select “Schedule of Classes,” then the term/year. Courses are listed by academic unit.
• The program uses specific section codes for registration, which are listed on the sample plan of study.

Sample Plan, Fall Admission

Sample Plan, Spring Admission

Courses

For a listing of program courses, see the Graduate School Catalog program requirements for Z109 and Z135. For course descriptions that include prerequisites or corequisites, see GEOG.

Overall

• Program offers two learning options: in person (major code Z109) or online (major code Z135). The curriculum is identical in each delivery format.
• Uses the term academic calendar with classes held each 12-week term: I (fall), II (winter), III (spring), IV (summer).
• Classes are held weekday evenings (e.g., after 5:00 p.m.) to accommodate the working professional’s schedule.
• Instruction provided by University of Maryland faculty and professionals in the field. 

In-Person Learning 

• Classes meet in UMD College Park campus classrooms, offering a focused, distraction-free learning environment. 
• Instructors present dynamic and interactive seminar-style instruction.
• Lectures are video archived. Students who are unable to attend a class session can review the session at their convenience.
• Students enrolled in a program that features in-person instruction are required to submit the University’s Immunization Record Form prior to the first day of their first semester/term. See Health Requirements. 

Online Learning

• Using advanced audio and video technology, UMD’s online learning environment delivers dynamic and interactive content. 
• Featuring convenience and flexibility, online instruction permits asynchronous or synchronous participation.
• Lectures are video archived. Students who are unable to attend in real time can review the session through asynchronous participation.

Upon successful completion, graduates will have mastered the following competencies:

• Understand the fundamental nature of geospatial intelligence and analysis, including the core theory, methods, and protocols for gathering and management of geospatial intelligence data, analyses, and visualization of those data.
• Understand the use of the resulting products in operational settings for applied geospatial intelligence.
• Understand the ethical treatment of data and analysis throughout those procedures.