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Advanced Remote Sensing

GEOG XL 182C

Develop an aptitude for integrating various remote sensing and geospatial analysis workflows/datasets into one unified cloud-based coding environment.

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What you can learn.

Gain an overview of advanced remote sensing concepts and applications
Examine linear models in remote sensing as a foundation for promoting understanding of more complicated machine learning models
Understand the fundamentals of machine learning in remote sensing as implemented in GEE (Random Forest models for image classification)
Explore advanced machine learning models not available in GEE (PointNet models for point cloud classification)

About this course:

The main objective of this course is to introduce advanced remote sensing topics in the realm of cloud computing. To promote collaboration in research and interoperability of different programming languages and data sources, we will be using the Google Earth Engine Python API running on Google Colab (GEE JavaScript API will only be used for reference purposes). This advanced course in remote sensing has six units: 0) Introduction; 1) Advanced GEE operations; 2) Modeling in GEE; 3) Machine learning in GEE (raster); 4) Machine learning in KERAS (point clouds); and 5) Open topics. All units will also include lab sessions with code examples to facilitate the transition from working in various platforms using different programming languages to working in one unified cloud-based coding environment. The ultimate goal is for you to be comfortable integrating different remote sensing and geospatial analysis workflows and datasets from different sources into one unified cloud-based coding environment to enhance efficiency and to promote collaboration.

Prerequisites

Recommended prerequisite: GEOG XL 182A: Introduction to Remote Sensing (or equivalent)