This document provides an outline for a lecture on Google Earth Engine, an online platform for planetary-scale geospatial analysis. It discusses the history and motivation for Earth Engine, including streamlining the traditional remote sensing workflow. It also previews demonstrations of Earth Engine's code editor, integrations with QGIS and Jupyter Notebooks, and case studies. Key points are Earth Engine's large public archive of analysis-ready Earth observation data, computational infrastructure for distributed processing, and programming interfaces including JavaScript and Python.

1. CC BY 4.0
Introduction to Google Earth Engine
Ujaval Gandhi
ujaval@spatialthoughts.com
End-to-End Google Earth Engine

2. Lecture Outline
● History and Motivation
● Earth Engine Timelapse
● Live Demos
○ Code Editor
○ QGIS
○ Jupyter Notebook
● Case Studies and Example Applications

3. The Classic Remote Sensing Workflow
Download data
Odd file formats
Metadata
Bad/missing data
Clouds & shadows
Atmosphere & haze
Calibration
...
This can be done
once ..
...so scientists can
focus on this.
Data science!
Data Prep

4. Google Earth Google Earth Engine
3D Viewer for high-resolution imagery.
Targeted for everyone!
Cloud-based platform for remote
sensing. Targeted for scientists and
researchers

5. Deriving Information from Earth Observation Data

6. Image © NASA

7. Spatial Thoughts
“Often it turns out to be
more efficient to move the
questions than to move the
data.”
-Jim Gray (1944-2007)

8. Large public repository of EO Analysis-ready data
70 petabytes, growing daily
~1 Petabyte/month added
>900 datasets
~100 datasets / year added

9. Google Computational Infrastructure
Large pool of servers, co-located with data
Allows for cloud-based distributed computing
500 Million CPU hours / year
Image © Google

10. Powerful API
Javascript: Web-based IDE for interactive
analysis
Python: Interactive and collaborative Jupyter
Notebook environment via Google Colab
3rd Party Integrations: QGIS, R ..