Geospatial Systems Engineering (GSEN)

An applied research group project in geospatial surveying engineering from problem definition to implementation in an area provided by faculty in the course of study. Fall, Spring, and Summer.

This course is for Geospatial Systems Engineering MS students choosing the thesis option. Preparatory and developmental research for the graduate thesis resulting in the preliminary design and formal proposal of the graduate project. This thesis proposal must be reviewed and approved by the project chairperson to receive credit. Offered on a credit/no-credit basis only.

This course is for Geospatial Systems Engineering MS students choosing the thesis option. Students will register for this course after completing GSEN 5397 Thesis I: Thesis Proposal. This course is only credit/no credit.

Prerequisite: GSEN 5397^*.
^* May be taken concurrently.

This course is for Geospatial Systems Engineering MS students choosing the thesis option. Students will register for this course after completing GSEN 5398 Thesis II: Thesis Research. This course is only offered on a satisfactory/unsatisfactory (S/U) basis only, with grade of IP until completed. Credit will not be recorded until thesis is accepted by the Graduate Project Committee. May be repeated for credit. Offered Fall, Spring, and Summer semesters.

Prerequisite: GSEN 5398.

Geospatial data management and analysis is a fundamental activity in describing, documenting, and modeling the built and natural environment. This course examines the use of various types of geospatial data, including remote sensing data, for characterization of geospatial phenomena. Topics covered include geodetic datums and coordinate systems, digital representation of geospatial data, positional accuracy and error propagation, spatial analysis and modeling techniques, and emerging topics such as geospatial AI. A GIS (ArcGIS Pro) will be used to investigate and visualize patterns and relationships using various types of geospatial data sets. Familiarity with basic concepts of probability and statistics; familiarity with calculus and matrix algebra is helpful for some topics though not required; experience working with ArcGIS or other GIS software is helpful.

Course teaches programming techniques in geospatial fields, such as how to automate GIS tasks using Python and other scripting languages. Automation can make your work easier, faster, and more accurate, and knowledge of a scripting language is a highly desired skill in GIS analysts.  Fall.

Handling, processing and analyzing spatial data in an open and reproducible way is critical in the emergence of geospatial data science. Various open source packages and tools for geospatial data and process are available and they provide an effective solution for flexibility, reproducibility and transparency in geospatial research and analysis. This course focuses on various programming skills in handling and manipulating spatial data through open source environments. Creating spatial database and queries, exploring spatial data, modeling spatial data, and visualizing spatial data through open source packages will be covered.

This course will focus on spatial database principles and the practical skills of design, implementation, and use of spatial databases. This course will first cover fundamentals of relational database design, and then focus on design and management of spatial databases utilizing geodatabase models. In addition, case studies of geodatabase design models in several applications will also be covered. This course is intended for students who want to design, create, maintain and manipulate data from a geospatial database. Spring.

Geospatial data mining is the process of automatically discovering interesting and useful spatial patterns in large geospatial datasets. This course begins by covering fundamental concepts and techniques in data mining. Specific topics covered include classification, association analysis, and cluster analysis. It then focuses on using these data mining techniques for handling spatial, temporal and spatial-temporal data. In addition, the data mining tools to implement applications in geoscience will also be covered. Spring.

Introduces the fundamentals of mapping with small Unmanned Aircraft Systems (sUAS) using digital imaging sensors to produce high resolution, accurate geospatial surveying products. The course will cover the full spectrum of UAS mapping including technology, current regulations, operational factors, flight design, photogrammetric data processing, and data fidelity. Supporting concepts will include georeferencing and ground control, 3D reconstruction with structure-from-motion photogrammetry, orthorectification and image mosaicking, accuracy assessment, and current developments in UAS for geomatics. Processing and analysis workflows using commercial and open-source software will be conducted to transform UAS image sequences into geospatial data products, extract analytics, assess results, and optimize output. Spring.

Addresses the foundations and computational techniques of Global Navigation Satellite Systems (GNSS) and inertial measurement units (IMUs) for autonomous navigation applications. Specifically, the course will cover concepts and principles of GNSS signal structures and the derivation of observables; error sources and corrections; point, differential, and kinetic positioning techniques; IMU linear and angular dynamics modeling; mechanization of inertial navigation and error propagation; global/local coordinate frames and conversion; and filtering techniques for GNSS/IMU integration. The course also covers current and future capabilities of emerging geopositioning systems as they relate to autonomous navigation and mobile devices. Fall.

This course will focus on geospatial statistics methods particularly multivariate statistics and applications of the statistical procedures to research geospatial problems. Research on geospatial problems often requires the application of multivariate statistical methods to produce new insight. Various existing statistic software is available to conduct multivariate statistical analysis, however, the interpretation of the results rely on solid understanding of statistic principles and theories. This course is intended for students who want to apply statistical methods to research geospatial problems. 

A review of the evolution of European cadastral systems and land records traditions and alternatives. Examination of the goals and purposes of land tenure systems with attention to social, political, legal, economic, organizational, and technical issues. Exploration of U.S. modernization efforts and the problems of developing countries. Spring odd years.

A study of the current and emerging status of computer law in electronic environments. Covers issues related to: privacy, freedom of information, confidentiality, copyright, and legal liability; the impact of statue and case law on use of digital databases and spatial databases; and research of legal options of conflicts related to spatial data. Additional description: study of specific court cases specific to Texas boundary law. Introduction into International Boundaries and Treaties. Fall.

This course will focus on the theory, techniques, and applications of advanced geospatial analytics. Topics covered include spatial point patterns, network analysis, area objects and spatial autocorrelation, and spatial interpolation. New approaches to geospatial analytics will also be covered. This course emphasizes the methods and the applied side of geospatial analytics that can be useful in students' own theses or projects for their current or potential employers.

This course will ensure that students understand and apply cartographic theory for visual communication and visual thinking, and be able to create, evaluate, and critique reference and thematic maps using GIS software. Fall.

A study of the analytical and systems engineering foundations of airborne photogrammetry and geodetic imaging technologies for 2D and 3D mapping of natural and built environments. The course covers principles of digital imaging, camera calibration, stereo and multi-view photogrammetry, analytical photogrammetry, structure-from-motion, light detection and ranging (lidar) systems, and emergent scanning and imaging approaches. The course also details photogrammetric and lidar data processing, point cloud analysis, and applications.

Addresses the interpretation, processing and analysis techniques of remotely sensed data acquired by orbital and sub-orbital platforms. Physical principles and imaging mechanisms, remote sensing systems, data characteristics, image processing, and information extraction methods will be covered. Topics include passive optical imaging with multispectral, hyperspectral, and thermal sensing; active imaging with radar sensing; image corrections and rectification; spatial/frequency transforms and image filtering; image classification and feature extraction; and image processing with machine learning techniques. Applications in the course will be focused on geomatics and monitoring of natural and built environments. Fall.

Variable content study of specific areas of  geospatial surveying engineering. May be repeated for credit when topics vary. Offered on sufficient demand. 

Seminar in reading and critical evaluation of academic literature in the fields relating to geospatial engineering. Research methods for geospatial engineering will be introduced. Student will design, implement, and evaluate an advanced, contemporary geospatial engineering technology to solve a geospatial problem.

Study in areas of current interest. A maximum of 6 SCH of approved Directed Independent Study may count toward the MS degree.