Graduate Certificate in Remote Sensing

The program is offered through the Department of Geographical Sciences in the College of Behavioral and Social Sciences. Geospatial Information Sciences provides the most up-to-date education on geospatial technology, theory and applications.  

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 Kristen Halliday, Assistant Director, via email: khallida@umd.edu.

The Graduate Certificate in Remote Sensing, In Person (Z151) has a 12-credit, 4-course curriculum that covers all major aspects of remote sensing including digital image processing and analysis; working with Lidar, drones for data collection, and also computer programming that is critical for data processing and analysis. 

• Concentrates on the science and technology for collecting, processing, analyzing, and visualizing geospatial data through remote sensing platforms such as satellite images, aerial photos, and drone images.
• Only offered via in-person learning.
• Can be completed in twelve months of continuous part-time enrollment. See Designation of Full-time/Part-time Status.

Courses

Below is a listing of all program courses. For a detailed course description that includes pre-requisites or co-requisites, see The Graduate School Catalog, Course Listing as follows: GEOG Course Descriptions.

Plan of study includes three 3-credit core courses (9 credits) and one 3-credit electives (3 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 services, 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

Overall 

• Uses the term academic calendar with classes held each 12-week term: I (fall), II (winter), III (spring), IV (summer).
• Instructors present dynamic and interactive seminar-style instruction. 
• 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. 
• Classes are held weekday evenings (e.g., after 5:00 p.m.) to accommodate the working professional’s schedule
• 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. 

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

• Understand the big picture of remote sensing as a disciplinary field, with a good understanding of its history, current state, and future development trend. 
• Grasp of the connections between different geospatial technology components such as GIS, remote sensing, computing, and emerging software and hardware options, e.g. drones and artificial intelligence. 
• Develop a good understanding of how remote sensing is applied to real-world problems.
• Develop proficiency in the following specific knowledge and skills:
  • Collecting spatial data through various remote sensing platforms
  • Processing remote sensing data using software such as ENVI
  • Be able to interpret remote sensing data
  • Be able to analyze remote sensing data
  • Be able to automate the data processing and analyzing through computer programming and scripting with languages such as Python, and
  • Have a good understanding about analysis of big data with high performance computing, especially spatial data in large volume and high velocity.