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GEOG 582 - AI in Spatial Data Science

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

This course focuses on the scientific and technical challenges that lie at the intersection of spatial data science and artificial intelligence (AI). Geospatial data and geographic visualizations are not readily supported by contemporary AI approaches, but development in this area of GeoAI is rapidly advancing. Spatial reasoning, spatial analysis, and cartographic design are all potentially aided via the use of AI in the near future. Students will engage with key theories, methods, and systems to develop new workflows that enable geospatial problem solving with the support of AI. Students will engage with emerging literature to contextualize and critique ongoing scientific progress towards achieving GeoAI goals. They will also develop customized solutions for AI-supported spatial data science tasks through lab exercises. These labs will allow students the opportunity to create and evaluate AI-supported spatial data science workflows with an emphasis on reproducibility and ethical responsibility. A culminating project in the class will allow students to create a new AI-supported spatial data science solution for a real-world application.

Objectives

Students who excel in this course are able to:

  1. Evaluate and justify the selection of the appropriate artificial intelligence methods to complete spatial data science analysis tasks.
  2. Create workflows that integrate artificial intelligence approaches with spatial analysis methods to address key problems in spatial data science.
  3. Develop scientific programming skills that integrate state-of-the-art coding practices in support of advancing the AI-supported spatial data science.
  4. Synthesize, analyze, and visualize multiple types of spatial data using artificial intelligence methods in support of complex problem-solving.

Required Materials

Typically, there are no required materials for this course. If this changes, students will find a definitive list in the course syllabus, in Canvas, when the course begins.

Prerequisites

GEOG 485, GIS Programming and Software Development

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

Achievement of educational outcomes identified above will be assessed through the evaluation of students’ research, technical, and design deliverables. Individual educational outcomes will be assessed using rubrics that clearly map student performance to levels of achievement. Students’ grades will be determined based on the following five components: 

  • Laboratory Assignments (40%)
  • Collaborative Workflow Development (10%)
  • Readings and Discussion Activities (10%)
  • Literature Review and Presentation (15%)
  • Semester Project (25%)

Course Schedule

This course is 10 weeks in length.