A master’s degree in Civil Engineering with a focus on geospatial engineering allows students to build advanced expertise in collecting, analyzing, modeling, and communicating spatial data to support engineering and infrastructure decisions. Graduate study at Michigan Tech helps students move beyond undergraduate fundamentals to develop deeper technical skills in GIS, GNSS, LiDAR, remote sensing, photogrammetry, and spatial analysis.
This degree is a strong next step for students who want to specialize in geospatial technologies, strengthen their technical problem-solving skills, and prepare for advanced roles in infrastructure, environmental systems, mapping, and data-driven engineering.
Earn $10,000 More Annuallywhen compared to undergraduate degree holders.
1 year experiencetowards your Professional Engineering License.
Sample Course Plan
This sample course plan is a sample, and adjustments may be required due to curriculum changes. Students should work with their advisor to develop their individual plan. A full list of graduate course descriptions is available.
Assumed Student Background
The sample course plan shown below was designed assuming that a student has an undergraduate degree in engineering, surveying, geography, or natural sciences
Requirements: 30 credits minimum (12 maximum credits at 3000-4000 level; 18 credits at 5000 level
Sample Coursework List
| Course | Credits | Semester |
|---|---|---|
| SU 5140 – Photogrammetry & UAV Mapping | 4 | Fall |
| SU 5022 – Geodetic Positioning | 3 | Fall |
| SU 5060 – Geodesy | 3 | Spring |
| SU 5541 – Close-range Photogrammetry | 3 | Spring |
| SU 5640 – Introduction to Remote Sensing | 3 | Spring |
| FW 5550 – Geographic Information Science and Spatial Analysis |
4 | Fall |
| FW 5540– Remote Sensing of the Environment | 3 | Fall |
| FW 5541- Remote Sensing of the Environment Lab | 1 | Fall |
| Systems Elective | 3 | |
| Graduate Elective | 3 |
Systems Elective
| Course | Credits | Semester |
|---|---|---|
| CEE 5710 – Modeling and Simulation Applications | 3 | Fall |
| CEE 5730 – Probabilistic Analysis and Reliability | 3 | Fall |
| CEE 5740 – Introduction to System Identification | 3 | Spring |
| CEE 5760 – Optimization Methods | 3 | Spring |
Sample Graduate Electives
| Course | Credits | Semester |
|---|---|---|
| MA 5701 – Statistical Method | 3 | Fall/Spring/Sum |
| MA 5627– Numerical Linear Algebra | 3 | Spring |
| MA 5631– Advanced Numerical Linear Algebra | 3 | Fall |
| MA 5741– Multivariate Statistical Methods | 3 | Spring |
| CS 5841 – Machine Learning | 3 | Spring |
| EE 5522 – Digital Image Processing | 3 | Fall/Spr |
| FW 5553 – Python Programming for ArcMap GIS | 3 | Fall |
This course plan is meant to serve as a sample for a student interested in pursuing a coursework-only MSCE degree. This plan may not be appropriate for all students, nor is it necessary for a student to follow this schedule to earn a coursework-only degree. Student-specific goals and prior education must be considered and consultation with faculty members is required. Consult with instructors before enrolling in courses that are outside of the Department to ensure that the course will be consistent with your goals and background since sometimes other courses may provide more value to the student. All MSCE degree requirements and rules set forth by the Department and the Graduate School must be met in order for a student to finish the program.