Course List

The MPS GEOINT program encompasses a 30-credit (10 units of 3-credit courses) course structure comprising five core courses and a selection of five courses among electives. The GCPS GEOINT program entails 12 credits from four courses (two core and two elective courses). The GCPS GEOINT is considered a subset of the MPS GEOINT: the credits earned from a GCPS GEOINT can be transferred towards the MPS GEOINT.

Courses offered in MPS and GCPS GEOINT expose students to material far beyond the existing offerings in geospatial information sciences (GIS) at UMD, and they cater to an entirely new population seeking employment and skills in the intelligence industry. MPS and GCPS GEOINT provide us some “cover” at the higher end of the geospatial intelligence job market and specifically help UMD to distinguish itself from a growing cohort of university programs that are offering basic software-oriented GIS courses at the lower-end of the market.

Courses are delivered in a hybrid format: Instructors present lectures and lead discussions tangibly in a classroom setting, while also streaming the lectures Online. Students that can attend in person may do so, while those that require access remotely can also participate (via Adobe Connect). Similarly, laboratory sessions may be attended tangibly, or students may access instruction remotely using video conferencing and virtual machine access to our software and data at UMD. International students, however, are limited in the way of taking classes. According to F-1 Visa regulations by the United States Citizenship and Immigration Services (UCSIS), only one "online" or "distance education" course per semester can be counted toward the student’s full course of study per academic period, and all other coursework must be delivered in a "contact" classroom (we are currently unable to accept international applications.)

Courses are scheduled in evenings (e.g. 5:30 pm - 8 pm) to accommodate working professionals. 

Courses offered in GEOINT programs are also available for non-degree seeking students. For more information on how to register as non-degree seeking students, please visit non-degree-admissionsNon-degree seeking students may transfer a maximum of nine (9) credits to an MPS or Graduate Certificate program, contingent on admission to the degree or certificate program and on the approval of the faculty in the program.

Program Course List:

The MPS and GCPS in GEOINT consist of the following courses:

Type
Course #
Course Title
Credits
Core
Fundamentals of Geospatial Intelligence
3
Core
Advances in Geographic Information Science and Remote Sensing
3
Core
Geospatial Intelligence Systems and Platforms
3
Core
Algorithms for Geospatial Intelligence Analysis
3
Core
Capstone Project: Problem-Solving in Geospatial Intelligence
3
Core*
Programming and Scripting for GIS*
3
Elective
Big Data Analytics
3
Elective
Geospatial Intelligence Networks
3
Elective
Open Source Intelligence
3
Elective
Hazards and Emergency Management
3
Elective
Mobile and Social Geocomputing
3
Elective*
Spatial Statistics*
3
Elective*
Web Programming*
3
Elective*
Advanced Remote Sensing Using Lidar*
3

*Note: Courses* are shared from MPS GIS program.

Courses are subject to updates with the emergence of new technologies in GEOINT industry.

Prerequisite Course List:

To get admission, the applicants are not required to have any GIS, remote sensing, and statistics background. However, before they can start the program, they have to meet the prerequisites. For students applying for Graduate Certificate track, only GIS and statistics are required as prerequisites. For students applying for the Master's degree track, they must have GIS, remote sensing and statistics background. These prerequisite courses (GEOG506, GEOG579, and GEOG579B) are offered online during the summer proceeding the fall term when the program starts. You can either take these prerequisite courses here in the University of Maryland or the equivalent courses in other institutions. The prerequisite courses do not count toward the degree.

Type
Course #
Course Title
Credits
Prerequisite
Introduction to Quantitative Methods for GIS
3
Prerequisite
Introduction to GIS
2
Prerequisite
Introduction to Remote Sensing
2
Prerequisite
Introduction to Computer Programming
2
 
Course Descriptions

GEOG661: Fundamentals of GEOINT
Geospatial Intelligence (GEOINT) is the collection, analysis, visualization and dissemination of geospatial information to support decision-making.  This course introduces the fundamental knowledge required to become a successful GEOINT practitioner, including the history of the GEOINT discipline, the intelligence applications of remote sensing and Geographic Information Systems (GIS) technologies, and how GEOINT products are used to support national security and humanitarian missions.  Upon completion of this course you will understand the roles that technology, policy, doctrine, government, and industry play in shaping the Geospatial Intelligence discipline, and develop the technical knowledge and domain expertise to create basic GEOINT products that provide context for decision makers. 

GEOG662: Advances in GIS and Remote Sensing
Assuming a basic understanding of geographic information systems and services, and remote sensing techniques, this course focuses on state-of-the-art advances in geographic information science and remote sensing as they support geospatial intelligence. The course will focus on synergies between GIS and remote sensing in informatics, computer science, and spatial engineering, and their application to problem domains in human systems, physical systems, and cyberspace. Advances in GIS presents recent advances regarding fundamental issues of geo-spatial information science (space and time, spatial analysis, uncertainty modeling and geo-visualization), and new scientific and technological research initiatives for geo-spatial information science (such as spatial data mining, mobile data modeling, and location-based services). Advances in remote sensing will provide the opportunity to understand and work with latest developments in the Remote Sensing datasets. The curriculum covers a wide range of remote sensing data interpretation and their processing techniques.

GEOG664: GEOINT Systems and Platforms
There are numerous systems and platforms that support the collection, visualization and dissemination of Geospatial Intelligence (GEOINT).  Platforms such as satellites and aircraft carry sensors systems that can detect both physical and man-made objects on the earth. Ground-based processing systems are used to analyze and visualize sensor data, and also to create and disseminate GEOINT products that guide decision-making. In this course you will learn how to develop and implement source-to-screen GEOINT workflows, and will understand how to use a system of systems approach to describe the programmatic and technical strengths and weaknesses of many different GEOINT systems and platforms.

GEOG665: Algorithms for GEOINT Analysis
With increasing sources and platforms of geospatial and imagery data, GEOINT analysts face new challenges in data exploitation and analytics. This course focuses on communicating the knowledge and capabilities that allow GEOINT analysts to be more efficient in analyzing and understanding the activities, relationships, and patterns discovered from these GEOINT sources. The purpose of this course is to expose students to fundamental algorithms in geospatial intelligence and their application in methodological and substantive domains, and their implementation in computer programs and software systems. This course provides an introduction to theoretical and applied aspects of GEOINT systems and quantitative methods with a focus on spatial analysis. Emphasis will be placed on the analysis of continuous and discrete geographical data for spatial problem-solving in both the human and physical spatial sciences. We will explore algorithms, data structures, and advanced computational topics. Implementation of algorithms will be explored through pseudo-code and a variety of scripting, data access, and programming languages.

  • Prerequisites: Students should have background in GIS (GEOG 662)
  • There is a $40.00 lab fee for this course
  • 3 credits [Back To Course List]

GEOG697: Capstone Project
The Capstone is an independent research project that demonstrates competence in geospatial intelligence technologies. This project can originate from an internship, from relevant work at a current or past employer, or can be developed in conjunction with GCIS faculty. The student will prepare a project report and presentation which shall contain an executive summary, background information including a literature review and establishment of requirements, a detailed technical description of the project data and methods, a discussion of results obtained, and final conclusions and recommendations. The final project submission will include all data, computer code and/or workflow documentation required to replicate the project results. In completing this project, students develop a concrete example of how GEOINT technologies can be applied to solve real-world problems, and begin developing a portfolio that can be presented to potential employers.

GEOG663: Big Data Analytics
This course is designed to introduce statistical analysis over big data sets (and tackling big data problems), primarily in geography and spatial sciences, but with broader appeal throughout the socio-behavioral sciences. Students will be introduced to a range of methods that can be applied to the exploration, modeling, and visualization of big quantitative data. This course explores data fusion, statistical analysis, and data-mining for geospatial and non-geospatial data in structured and unstructured form, with an emphasis on large silos of data across diverse sources and assumptions.

GEOG680: Geospatial Intelligence Networks
Networks are an important part of the Geospatial Intelligence (GEOINT) cycle, from the sensor networks that are used to collect raw geospatial information to the telecommunication networks that are used to disseminate finished GEOINT products.  Transportation networks, computer networks, social networks, and many other man-made and natural features can also be characterized by a link-node network topology, and can be studied using network science methods.  Upon completion of this course you will be able characterize and classify real-world GEOINT networks and their components, understand network dynamics including routing, scalability, and robustness, and be able to apply engineering methods for network design and network analysis.

GEOG682: Open Source Intelligence
Open Source Intelligence (OSINT) is information that is publicly available which is collected and analyzed to support decision-making.  The collection and analysis of OSINT is often considered to be the first step in developing an “all-source” intelligence product, where OSINT is fused with Geospatial Intelligence (GEOINT), Signals Intelligence (SIGINT), and Measurement and Signature Intelligence (MASINT), and Human Intelligence (HUMINT).  In this course you will learn about the sources, ethics, and methods that are associated with OSINT, and will also develop knowledge and skills related to open-source geospatial technologies and organizations such as the Open Geospatial Consortium (OGC).

GEOG683: Hazards and Emergency Management
Timely and accurate Geospatial Intelligence (GEOINT) is essential for protecting people from hazardous events such as floods, wildfires, tsunamis, hurricanes, industrial accidents, and terrorist attacks. GEOINT plays a critical role in all four stages of emergency management: preparedness, mitigation, response, and recovery.  The use of remote sensing and Geographic Information Systems (GIS) before, during, and after Hurricane Katrina and the 9/11 terror attacks are two of the case studies that are discussed during this course.  You will develop a deeper understanding of the emergency management successes and failures that occurred during these historic and deadly events, and learn the technical skills to develop and disseminate GEOINT products that support decision-making at all four stages of emergency management.

GEOG686: Mobile and Social Geocomputing
Designed as an introduction to mobile GIS, to the programming concepts underlying mobile GIS development, and more importantly, to the design and implementation of a mobile GIS application. Covers how to develop, test, and publish mobile GIS native apps working across two mobile platforms: Android and iOS. Leverages the capabilities of JavaScript, Swift, Google maps, ArcGIS Server and runtime SDK to developing and publishing mobile GIS apps.

GEOG656: Programming and Scripting for GIS
This course teaches programming and scripting for GIS users. The concepts of scripting and object-oriented programming using the Python programming language are reviewed. This course teaches students to design clearly structured programs and introduces ArcPy, a library providing access to ArcGIS geoprocessing tools. ArcPy includes a series of modules such as data access, mapping, spatial analysis, and network analysis. Students will develop geoprocessing programs to edit, query, manipulate, and analyze spatial data (both vector and raster data) with Python, ArcPy, and other modules like NumPy.

GEOG651: Spatial Statistics
This course is about quantitative analysis of spatial data. It is intended to provide a broad survey of various spatial statistic methods. The course is geared towards helping students: (1) develop an understanding of the important theoretical concepts in spatial data analysis; and (2) gain practical experience in the application of spatial statistics to a variety of social and environmental problems using the advanced statistical software. This course covers five broad topical areas: (1) point pattern analysis; (2) area data analysis; (3) continuous data analysis; (4) spatial sampling; and (5) multivariate spatial and temporal analysis.

  • Prerequisites: Students should have a background in elementary statistics and introductory GIS
  • There is a $40.00 lab fee for this course
  • 3 credits [Back To Course List]

GEOG657: Web Programming
Component-based web server design and efficient session and secure access management have become challenges to provide fast, robust, and flexible GIS services on the Internet.  This course is designed to teach fundamental techniques required in developing both client-side and server-side web application for not only GIS but also non-GIS applications. This course covers web design and static web generation using HTML5 and CSS, client-side programming with JavaScript, and dynamic web development using PHP and MySQL. Basic web design using HTML, XHTML, CSS, etc. is helpful, but not required.

  • Prerequisites: Students should have a background in basic computer programming (GEOG579C or GEOG656)
  • There is a $40.00 lab fee for this course
  • 3 credits [Back To Course List]

GEOG660: Advanced Remote Sensing using Lidar
This course will expand on remote sensing concepts with a focus on light detection and ranging (lidar) technology. Lidar, also known as laser scanning, is an active remote sensing tool that can produce high-resolution point clouds. This course will cover the fundamentals of lidar, explore current developments in lidar technology, and discuss different applications where it is being used. Students will get hands-on learning about lidar data management, processing, and analysis. It is recommended that students have some background in spatial modeling and computer programming.

  • Prerequisites: Students should have a background in passive remote sensing analysis.
  • There is a $40.00 lab fee for this course
  • 3 credits [Back To Course List]

GEOG506: Introduction to Quantitative Methods for the Geographic Sciences
This course provides an introduction to quantitative methods for geographic and environmental sciences. The class covers the fundamentals of statistical analysis including data display, data description and summary, statistical inference and significance tests, analysis of variance, correlation, and regression. The main goal of this class is to provide a foundation in the quantitative analysis of spatial and other data. Students will 1) develop an understanding of important theoretical concepts in statistical analysis and 2) gain experience in the application of statistics to spatial and other data using statistical software.

  • Prerequisites: Admission to the MPS GEOINT program. Credits received for this course will not count toward the degree.
  • 3 credits [Back To Course List]

GEOG579 and GEOG579B: Introduction to Geographic Information Systems and Remote Sensing
An introduction to geographic information systems and remote sensing. Topics include methods of obtaining quantitative information from remotely-sensed images, interpretation of remotely-sensed images for spatial and environmental relationships, characteristics and organization of geographic data including spatial data models for thematic mapping and map analysis and use of geographic information systems in society, government, and business. Practical experience with remote sensing software and geographic information systems.

  • Prerequisites: Admission to the MPS GEOINT program. Credits received for this course will not count towar the degree.
  • 2 credits each [Back To Course List]

GEOG579C: Introduction to Computer Programming
This course is an introduction to computer programming using the Python programming language. It is recommended for students before they enroll in the more advanced programming courses. This course teaches students the fundamentals concepts of computer science. The students will learn about the core components of a computer program, such as data management, conditional statements, iterative statements, and file processing. Students will develop programs the purpose of automating tasks and assisting with data analysis.

  • Prerequisites: Admission to the MPS GEOINT program. Credits received for this course will not count toward the degree.
  • 2 credits [Back To Course List]

 

 

Master of Professional Studies and Graduate Certificate in Professional Studies in Geospatial Intelligence