RMS 5105. Fundamentals of Remote Sensing. (2 Hours)

Covers remote sensing principles, datasets, fundamental interpretation, and analysis in four general categories: processes and theories involved in remote sensing, various sensor types and platforms, applications of remote sensing, and methods of remote sensing as applied to analyzing images and extracting desired information.


RMS 6275. Introduction to Machine Learning for Image Data. (2 Hours)

Explores a range of machine learning routines, including image classifications and clustering, PCAs, and data reduction. Presents exercises corresponding to concepts introduced in weekly lessons. Focuses on computer thinking, algorithms involved in preprocessing, spectral and spatial enhancement, spatial analysis, and linear transformations. Utilizes a variety of data types. Offers students an opportunity to experience the journey of geospatial image data from its origin (raw data) to its end (transformation) in the context of the process, scope, and real-world scenarios.

Prerequisite(s): RMS 5105 (may be taken concurrently) with a minimum grade of C-


RMS 6280. Automated Feature Extraction for the Geospatial Professional. (2 Hours)

Introduces machine learning and automated feature extraction software and how it is utilized for image interpretation. Explores various techniques and workflows associated with collecting features of interest from multiple data sources, e.g., aerial and satellite imagery, LiDAR, and elevation data. Students use AFE software to solve real-world problems in exercises corresponding to concepts introduced in weekly lessons. Offers students an opportunity to learn how to use feature extraction to create industry-standard analytical products and develop processing models for automation. Discusses the fundamentals of machine learning, supervised and unsupervised classification, hierarchical learning, postprocessing, cleanup, automation, modeling, and publication.


RMS 6962. Elective. (1-4 Hours)

Offers elective credit for courses taken at other academic institutions. May be repeated without limit.