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Courses

SIE Course Timetable

Courses in the Spatial Information Science and Engineering degree programs are typically offered once a year during the same semester each year.  For planning purposes, obtain the typical Annual Schedule of SIE Classes from the SCIS office.

The official schedule of classes for each semester is available through MaineStreet. If you do not yet have an account, click Log In and then under Quick Links, choose University of Maine term, and then select Class Search and then Search. Choose Course Subject (SIE) and Course Career (Graduate). All courses offered that semester should be listed. Independent study and thesis courses may be arranged with appropriate professors at any time.


SIE Course Materials and Syllabi

For a convenient point of access to materials for graduate courses offered to both online and on-campus students, see computing and information science courses online


SIE Graduate Course Descriptions

To view the official course descriptions, consult the Graduate Catalog > Graduate Courses > Prefix of SIE > Filter.

For the convenience of students, copies of graduate course descriptions from the 2015-2016 catalog are also provided below.

SIE 501 – Introduction to Graduate Research
Covers process of successful graduate research from identification of a researchable question, preparation of a thesis proposal, to completion or the research and its publication. Focus on engineering research methods for spatial information. Credits: 1

SIE 502 – Research Methods
Covers process of successful graduate research, including the written and verbal presentation of plans and results. Students formulate hypotheses, perform a literature search, write abstracts and introductions of research papers, learn about presentation styles and techniques, make two presentations (3-minutes and
10-minutes) about research proposals. Lec 1.
Prerequisites & Notes
SIE 501 and students must have selected a thesis topic. Credits: 1

SIE 503 – Principles of Experimental Design
This is an interdisciplinary course designed primarily for first year graduate students and advanced standing undergraduates who plan to engage in scientific research. The course covers topics in: (1) design of experiments, (2) modern experimental techniques and instrumentation, and (3) data collection, organization, and statistical analysis techniques.
Prerequisites & Notes
SIE 501 or permission. Credits: 1

SIE 505 – Formal Foundations for Information Science
Increases student’s understanding of the approach to information systems and science by formalisms. Draws on mathematics to increase familiarity with formal syntax and language, develops understanding and technical ability in handling structures relevant to information systems and science. Includes a review of fundamental material on set theory, functions and relations, graph theory, and logic; examines a variety of algebraic structures; discusses formal languages and the bases of computation.
Prerequisites & Notes
SIE or MSIS student or permission of instructor. Credits: 3

SIE 507 – Information Systems Programming
Programming for those envisioning careers focused on developing and managing information systems and databases as opposed to software design. Data structures, algorithms, and their analysis. Lec. 3.
Prerequisites & Notes
SIE or MSIS student or permission of instructor. Credits: 3

SIE 509 – Principles of Geographic Information Systems
Covers foundation principles of geographic information systems, including traditional representations of spatial data and techniques for analyzing spatial data in digital form. Combines an overview of general principles associated with implementation of geographic information systems and practical experience in the analysis of geographic information. Not open to those who have taken SIE 271.
Prerequisites & Notes
Graduate standing or instructor permission. Credits: 3

SIE 510 – Geographic Information Systems Applications
Introduces both conceptual and practical aspects of developing GIS applications. Covers application areas from natural resourse planning cthrough transportation, cadastral and land information systems and their spatial modeling requirements, and application development from requirement analysis to database design and implementation.
Prerequisites & Notes
ISE 201, or SIE 509 or permission. Credits: 3

SIE 512 – Spatial Analysis
Introduces students to techniques for spatial analysis. Covers methods and problems in spatial data sampling, issues in preliminary or exploratory analysis, problems in providing numerical summaries and characterizing spatial properties of map data and analysis techniques for univariate and multivariate data. Students will be responsible for completing several hands-on exercises.
Prerequisites & Notes
An introductory statistics course, graduate standing or instructor permission. Credits: 3

SIE 515 – Human Computer Interaction
Students are introduced to the fundamental theories and concepts of human-computer interaction (HCI). Topics covered include: interface design and evaluation, usability and universal design, multimodal interfaces (touch, gesture, natural language), virtual reality, and spatial displays.
Credits: 3

SIE 516 – Virtual Reality: Research and Applications
This course is designed to provide students with an overview of the basic principles of virtual reality (VR) and virtual environment technology (VET). The goal is to learn enough about the strengths and limitations of VR technology in order to be able to construct simple immersive environments as well as to understand the human factors and cognitive issues that should be considered when using this medium.
Credits: 3

SIE 525 – Information Systems Law
Current and emerging status of computer law in electronic environments: rights of privacy, freedom of information, confidentiality, work product protection, copyright, security, legal liability; impact of law on use of databases and spatial datasets; legal options for dealing with conflicts and adaptations of law over time.
Prerequisites & Notes
Graduate standing or instructor permission. Credits: 3

SIE 550 – Design of Information Systems
Cognitive and theoretical foundation for representation of knowledge in information systems and fundamental concepts necessary to design and implement information systems. Logic programming as a tool for fast design and prototyping of data models. Formal languages and formal models, conceptual modeling techniques, methods for data abstraction, object-oriented modeling and database schema design. Relational data model and database query languages, including SQL.
Prerequisites & Notes
Graduate standing or instructor permission. Credits: 3

SIE 554 – Spatial Reasoning
Qualitative representations of geographic space. Formalisms for topological, directional and metric relations; inference mechanisms to derive composition tables; geometric representations of natural language-like spatial predicates; formalizations of advanced cognitively motivated spatial concepts, such as image schemata; construction of relation algebras.
Prerequisites & Notes
SIE 451 or SIE 550. Credits: 1 or 3

SIE 555 – Spatial Database Systems
Covers internal system aspects of spatial database systems. Layered database architecture. Physical data independence. Spatial data models. Storage hierarchy. File organization. Spatial index structures. Spatial query processing and optimization. Transaction management and crash recovery. Commercial spatial database systems.
Prerequisites & Notes
SIE 550 and programming experience in Java, C++ or C. Credits: 3

SIE 557 – Database System Applications
Study, design and implementation of object-relational database system applications. Introduction to database systems. Integrating database systems with programs. Web applications using database systems. Final database project.
Prerequisites & Notes
SIE 507.  Credits: 3

SIE 558 – Real-time Sensor Data Streams
This course is an introduction into the technology of sensor data stream management. This data management technology is driven by computing through sensors and other smart devices that are embedded in the environment and attached to the Internet, constantly streaming sensed information. With streams everywhere, Data Stream Engines (DSE) have emerged aiming to provide generic software technology similar to that of database systems for analyzing streaming data with simple queries in real-time. Sensor streams are ultimately stored in databases and analyzed using scalable cloud technologies.
Prerequisites & Notes
Graduate standing, programming experience in Java, C++, or C, or permission of the instructor. Credits: 3

SIE 559 – Geosensor Networks
Readily available technology of ubiquitous wireless communication networks, the miniaturization of computing and storage platforms as well as the development of novel microsensors and sensor materials has lead to the technology of wireless geosensor networks (GSN). Geosensor networks have changed the type of dynamic environmental phenomena that can be detected, monitored and reacted to, often in real-time. In this course, we will survey the field of wireless geosensor networks, and explore the state of the art in technology and algorithms to achieve energy-efficient, robust and decentralized spatial computing.
Prerequisites & Notes
Graduate standing, programming experience in Java, C++, or C, or permission of the instructor. Credits: 3

SIE 565 – Reasoning With Uncertainty in Spatial Information Systems
Information systems and artificial intelligence approaches to uncertainty handling in spatial information systems. Typology of uncertainty: imprecision, inaccuracy and inconsistency. Representing and reasoning with spatial uncertainty in information systems. Logics of uncertainty, probabilistic and Bayesian approaches, Dempster-Shafer theory of evidence. Spatial vagueness. Handling conflicting information.
Prerequisites & Notes
SIE 451 or SIE 550, graduate standing or instructor permission. Credits: 3

SIE 570 – Spatial Cognition and Computing
Study of cognitive aspects for understanding spatial representations and reasoning processes. Cognitive models are studied and related to Artificial Intelligence Systems.
Credits: 3

SIE 571 – Pattern Recognition and Robotics
Pattern recognition algorithms classify input data based on statistical information. A mobile robot needs pattern recognition algorithms to make sense of its spatial environment based on sensor input. The course will introduce the mathematical framework of pattern recognition and present practical applications in robotics. The course will also cover supervised neural network learning algorithms.
Credits: 3

SIE 589 – Graduate Project
Directed study on a particular spatial information science topic and implementation of a related project.
Prerequisites & Notes
SIE Master Project Students. Credits: 3

SIE 590 – Information Systems Internship
Utilization of knowledge gained from the information systems graduate program within a business, non-profit or government organization and acquisition of practical training.
Prerequisites & Notes
Successful completion of nine credits of required courses in a school graduate program. Credits: 3-6

SIE 598 – Selected Studies in Spatial Information Engineering
Topics in surveying, photogrammetry, remote sensing, land information systems and geodesy. Content varies to suit current needs. May be repeated for credit.
Credits: 1-3

SIE 693 – Graduate Seminar
Presentations and discussions on term projects, literature reviews, current events, or thesis topics. Lec 1.
Credits: 1

SIE 699 – Graduate Thesis/Research
Graduate thesis or research conducted under the supervision of student’s advisor.
Credits: arranged

INT 601 – Responsible Conduct of Research
Key topics in conducting research responsibly. Guidelines, policies and codes relating to ethical research. Skills development for identifying and resolving ethical conflicts arising in research. Address case studies in the context of ethical theories and concepts.
Credits: 1