OUR DIGITAL GLOBE: AN OVERVIEW OF GISCIENCE AND ITS TECHNOLOGY

GEOG 379

Introduction to state-of-the-art technologies that capture properties of the landscape, which is critical to better characterize and understand environmental properties and change. Develop skills in geospatial systems applicable to a variety of research and industry fields. Includes an overview of unmanned aerial systems (drones), light detection and ranging (LiDAR), high-grade Differential GPS (DGPS), Global Navigation Satellite Systems (GNSS), virtual reality, optical sensors, geocaching, and geotagging.

GEOG 578

Application and use of GIS techniques in physical and human geography. Includes an introduction to a generic framework of GIS applications, case studies, and student projects. Cases range from urban and regional geography, to marketing geography, and to physical and environmental geography.

GEOG 365

An introduction to the breadth and practice of Geography. Introduces geographic perspectives, theories, themes, and research design. Covers the history of the discipline, applied quantitative and qualitative methodologies used in geographic research, and a selection of subfields within the discipline.

GEOG 370

A broad introduction to cartography emphasizing the theory and practice of map-making. Topics include the basics in mapping (e.g., scale, spatial reference systems, projections), data acquisition, key techniques for thematic mapping, and principles of cartographic abstraction and design.

GEOG 573

Geospatial big data is an extension of big data with an emphasis on the geospatial component. It is used to describe large volumes of georeferenced data about various aspects of the environment and society captured by millions of environmental and human sensors. An introduction to the theory, techniques, and analytical methods for geospatial big data. Methods for storing, processing, analyzing, and visualizing various types of geospatial big data using advanced Python programming will be introduced. Designed for students who have programming experience and want to reinforce their programming skills and learn AI and machine learning methods for solving geospatial big data problems.