GEOG 883 - Remote Sensing Image Analysis and Applications
This is a sample syllabus.
This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.
Overview
GEOG 883 is a graduate-level course focusing on remotely sensed data for geospatial applications. This course assumes that students have prior knowledge of the basics of remote sensing, mapping, and GIS, and have experience with geospatial software, particularly ArcGIS. Students will develop a strong understanding of the tools and techniques used to display, process, and analyze remotely sensed data. Upon completion of GEOG 883, students will be able to develop analytical workflows to derive products and extract information from remotely sensed data for a broad range of applications. The culmination of this course is an independent final project in which students will demonstrate their ability to apply new skills to a real-world situation of personal or professional interest.
Objectives
Students who excel in this course are able to:
- process remotely sensed data to make it useful in geographic information systems;
- perform image enhancement on remotely sensed imagery;
- extract information from remotely sensed data using a variety of manual and automated techniques;
- critically assess the strengths and weaknesses of remote sensing instruments and platforms for a variety of application scenarios;
- develop multi-step remote sensing workflows to solve problems in a variety of application areas;
- apply acquired knowledge and critical thinking skills to solve a real-world problem with appropriate remote sensing data and processing methods;
- clearly and concisely communicate findings from the analysis of remotely sensed data through the written word and graphical products.
Required Materials
The materials listed here represent those that may be included in this course. Students will find a definitive list in the course syllabus, in Canvas, when the course begins.
Required Texts
Campbell, James B., et al. (2023). Introduction to Remote Sensing, 6th edition. New York. The Guilford Press. ISBN 978-1462549405. A free online version is available through the Penn State Library.
Parece, Tammy, McGee, John, Campbell, Jim (2019). Remote Sensing with ArcGIS Pro. (Workbook). Virginia Tech. ISBN: 1797570986.
Required Software
All of the required software is available without cost to Penn State students registered for this class.
- ArcGIS Pro, Esri
- ArcGIS Online, Esri
- eCognition, Trimble
- Google Earth Engine
- 7-Zip (or similar)
- Screen Capture Utility
Prerequisites
GEOG 480: Exploring Imagery and Elevation Data in GIS Applications, GEOG 482: Making Maps that Matter with GIS, GEOG 483: Problem Solving with GIS or equivalent professional experience. Strong working knowledge of desktop geospatial software (e.g. ArcGIS) is expected of students who register for this course.
Expectations
We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 12 – 15 hours per week on class work.
Major Assignments
Students earn grades that reflect the extent to which they achieve the learning objectives listed above. Opportunities to demonstrate learning include:
- Online quizzes (12% of final grade)
- Online discussions (6% of final grade)
- Lab activities (56% of final grade)
- Final project (26% of final grade)
Course Schedule
| Week | Topic | Assignment |
|---|---|---|
| 0 | Orientation |
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| 1 | Working with Remotely Sensed Data |
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| 2 | Preprocessing of Remotely Sensed Data |
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| 3 | Emergent Earth Observation Sensors, Platforms, and Analytics |
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| 4 | Pixel and Object-Based Image Classification |
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| 5 | Rule-Based Geographic Object-Based Image Analysis (GEOBIA) Classification |
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| 6 | Change Detection |
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| 7 | Machine Learning and Classification of Remotely Sensed Data |
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| 8 | Thematic Map Accuracy Assessment |
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| 9 & 10 | Final Project: Remote Sensing Data Analytics: Addressing Contemporary Socio-Economic, Environmental, Urban, and Security Issues |
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