SIE Course Timetable

Courses in the Spatial Information Science and Engineering (SIE) programs are typically offered once a year during the same semester. For planning purposes, obtain the Annual Schedule of SIE Classes from the SCIS office. See also pages 11–14 of the Graduate Student Guide.

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

See also the suggested list of SIE graduate courses appropriate as electives for upper-level undergraduates and graduate students interested in gaining depth or breadth in Spatial Computing, Information Systems, and Data Science.

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SIE Graduate Course Descriptions

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 of the research and its publication. Focus on engineering research methods for spatial information. Credits: 1.
Prerequisites & Notes: None

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. Credits: 1.
Prerequisites & Notes: SIE 501 and students must have selected a thesis topic.

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. Credits: 1.
Prerequisites & Notes: SIE 501 or permission.

SIE 504 – The Beauty and Joy of Computing
This is an introductory course in computer science designed to prepare students with the skills and knowledge necessary to teach the first Advanced Placement (AP) course “Computer Science Principles”, but will also be useful for students wishing to integrate computer science concepts into other academic disciplines. The course covers the AP Principles Framework and Computational Thinking Practices. Credits: 3.

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. Credits: 3.
Prerequisites & Notes: SIE or MSIS student or permission of instructor, SIE 550 highly recommended.

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. Credits: 3.
Prerequisites & Notes: SIE or MSIS student or permission of instructor.

SIE 508 – Object Oriented Programming
Object-oriented programming represents the integration of software components into large-scale software architecture. This course introduces advanced programming skills and focuses on programming and design using a high-level object-oriented language, either Python or Java. The core concepts of object-oriented programming are examined and practical applications in the domain of data science and as seen in stacks, queues, lists, and trees are explored. Credits: 3.
Prerequisites & Notes: SIE 507 or permission of instructor.

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. Credits: 3.
Prerequisites & Notes: Graduate standing or instructor permission.

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

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. Credits: 3.
Prerequisites & Notes: An introductory statistics course and graduate standing or instructor permission.

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.
Prerequisites & Notes: None

SIE 516 – Interactive Technologies for Solving Real-World Problems
This course is designed to provide students with an overview of the basic principles of interactive design and immersive technology (virtual, augmented, mixed, and extended reality). The goal is to learn enough about the strengths and limitations of this technology, and the associated human factors, to design simple prototypes aimed at solving real-world problems. Credits: 3.
Prerequisites & Notes: Programming experience and graduate standing or instructor permission.

SIE 517 – Spatial Interaction Design
The main objective of this course is to provide a hands-on experience of interaction design research practice focusing on the interactive prototype construction. The principles and technologies of interaction design will be learned by adding expressive interactions to objects and spaces around us (spatial interactions). Interaction Design (IxD) discovers people’s needs, understands the context of use, frames product opportunities, and propose useful, usable, and desirable (usually digital) products. Interaction designers often work with narrative to explore and refine desired behaviors and user experience. This interdisciplinary course (projects based) will engage students with the fundamentals of interaction design and applied interaction design methods to shape behavior between people and products, services, and environments. First, we will select a specific location in a domestic setting (for example, the kitchen, dining room, office space, or the playground), then discuss and develop digital interactions for novel experiences. Credits: 3.
Prerequisites & Notes: Graduate standing or permission of instructor.

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. Credits: 3.
Prerequisites & Notes: Graduate standing or instructor permission.

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. Credits: 3.
Prerequisites & Notes: Graduate standing or instructor permission.

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. Credits: 1 or 3.
Prerequisites & Notes: SIE 550.

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. Credits: 3.
Prerequisites & Notes: SIE 550 and programming experience in Java, C++ or C.

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. Credits: 3.
Prerequisites & Notes: SIE 507.

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. Credits: 3.
Prerequisites & Notes: Graduate standing, programming experience in Java, C++, or C, or permission of the instructor.

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. Credits: 3.
Prerequisites & Notes: Graduate standing, programming experience in Java, C++, or C, or permission of the instructor.

SIE 580 – Ontology Engineering Theory and Practice
Ontologies are explicit specifications of information models and their semantics in formats that are interpretable by humans and computers. The course introduces the philosophical and logical foundations of ontologies and surveys formalisms, modern languages and methods for designing, analyzing and using ontologies. The stages of ontology development from conceptual design to ontology evaluation and verification are studied and practiced using concrete domains.
Prerequisites: SIE 505 or instructor permission. Credits: 3.

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

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. Credits: 3.
Prerequisites & Notes: Successful completion of nine credits of required courses in a school graduate program.

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 694 – Doctoral Seminar
This course advances the dissertation work of SIE doctoral students and PhD candidates. Students will work as peers to review each other’s progress with dissertation writing. Advancements over the last 12 months will be presented as poster and in a seminar talk, in order to maintain the momentum on making progress with the dissertation writing. Credits: 1.
Prerequisite: SIE 501, SIE 502, and SIE 693.

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

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Additional courses currently offered and appropriate for inclusion on Programs of Study and under review to receive permanent course numbers:

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.

SIE 598 – Selected Studies in SIE: Data Science Practicum (proposed permanently as DSE 510)
The Data Science Practicum introduces students to standard tools and methods used to explore, visualize, and analyze data. Students will become familiar with preprocessing and data cleaning, effective visualization methods and their application as pertinent to different data types and basic data analysis. Students will gain knowledge and experience through applying data science tools and methods to real world data sets. The course will be taught using Python.
Prerequisites and notes: grad program admission and SIE 507 or permission. Credits: 3.

COS 598 – Adv Topics in CS: Statistics Foundations for Data Science
An introduction and overview of statistical methods that are fundamental in understanding data science. Topics include: basic probability theory, Bayesian probability, random variable, expectation and variance, linear regression, cross validation, supervised and unsupervised learning, gaussian distribution, hypothesis testing, time-series/sequential data science, and processing and summarizing data. Credits: 3.
Prerequisites: college level statistics course.

COS 473/573 (COS 598) – Adv Topics in CS: Computer Vision
Practical introduction to machine learning. Computer Vision is an accessible sub-field of computer science rising in importance and accelerating on the strengths of machine learning methods that have become the 21st century model for artificial intelligence. In this course, we will explore the uses of tools and techniques to understand our world through computing using images as our data. The first half of the course will introduce machine learning and convolution neural networks for object recognition and classification, photogrammetry and reconstruction, and multimodal and hyperspectral imaging. As the course progresses, we will delve into the topics of image acquisition, mathematical analysis, the Fourier transform and frequency space, statistical pattern recognition, and other foundations of the field. This course is a fast-paced, hands-on, practical exploration of computer vision. Students from the class are organized into teams to work on a computer vision project. Credits: 3.
Prerequisites: COS 226 Intro to Data Structures.

COS 435/535 (COS 598) – Adv Topics in CS: Engineering Privacy in Software Systems
Introduces theory and practice for privacy, anonymity and compliance. Topics include: information privacy and multi-jurisdictional privacy compliance, privacy governance frameworks, privacy engineering lifecycle methodology, privacy by design, usable privacy, privacy and emerging technologies, anonymity techniques, differential privacy and private AI. Credits: 3.
Prerequisites: college level knowledge of IT or software development.