teaching

SLU

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Machine Learning for GIS and Remote Sensing

This course introduces the theory and applications of both statistical and deep learning-based machine learning methods for supervised learning tasks such as classification, segmentation, object detection, anomaly detection, and change detection for remote sensing data.
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Introduction to Programming for GIS and Remote Sensing

This course introduces the theory and applications of both statistical and deep learning-based machine learning methods for supervised learning tasks such as classification, segmentation, object detection, anomaly detection, and change detection for remote sensing data.
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Advanced Programming for GIS and Remote Sensing

A follow on to the Introduction to Programming in GIS and Remote Sensing course, this course introduces advanced topics in R, Python, and Javascript languages as they pertain to spatial analysis. Course introduces advanced spatial libraries such as fiona, geopandas, arcpy, and Folium in Python, in addition to learning advanced geographic data visualization techniques that leverage Python, Javascript, and web APIs. This course prepares students to be full stack geospatial programmers with both front-end and back-end programming skills in GIS.