Geospatial Big Data refers to large volume of georeferenced data coming from multiple sources such as remote sensing (various platforms), historic archives, contemporary monitoring programs (traffic control, industrial monitoring, weather stations), location-based services (mobile phone data, social media data, GPS trajectories). Together, all these sources form the flow of Big Data, which challenges capacity of GIS for fast and meaningful data analysis and processing. This course introduces the concepts/theories, techniques, and analytical methods of artificial intelligence for geospatial big data. Practical applications of geospatial big data using advanced Python programming will be introduced. Students who have received credit for Gog494 cannot receive credit for Gog594/Pln594. Prerequisites: Gog 408 or 508, or a similar introductory programming course on spatial data science using R and Python. Students should also have a basic understanding of statistics (equivalent to an introductory statistics course) and GIS (equivalent to an introductory GIS course). Please contact the instructor if you don't have these requirements, you may gain this background through self-learning.