2023-2024 Academic Catalog 
    Jul 20, 2024  
2023-2024 Academic Catalog [ARCHIVED CATALOG]

DSCI 215 - Applying Deep Learning to Earth Observation

Focusing on convolutional and recurrent neural networks, this course will provide an in-depth overview of key machine learning algorithms and their application to satellite imagery (especially for the task of semantic segmentation), including the full workflow required to acquire and process imagery, develop and train a model, and make and critically evaluate the resulting maps. The course will be strongly hands-on, and emphasize the use of programming (python), open image archives and EO analytical platforms (e.g. Google Earth Engine), and tools for creating open and reproducible workflows (git and GitHub).

For DSCI students, this course satisfies the capstone requirement.

Prerequisites: There are two sets of prerequisites for this course, one to ensure that students have sufficient background in GIScience and the other to ensure that they have enough programming experience.

GIScience Prereqs:
GEOG 293  or GEOG 383  with preference given to students that have taken GEOG 282  or GEOG 382  and GEOG 296  or GEOG 397  

Programming Prereqs:
Can be satisfied through two approaches -

Approach 1 - take at least one: DSCI 105  or DSCI 305  ; DSCI 125  or DSCI 304  ; CSCI 120  or CSCI 301  

Approach 2 - take at least two: IDCE 302  ; IDCE 30274  ; GEOG 246  or GEOG 346  


Determination regarding whether prerequisites can be waived or satisfied by other means may also be made by the instructors.

Anticipated Terms Offered: Annually