Title Card: Creator: Parisa Setayesh | Level: Intermediate | Category: Tools: Building Maps

Why this matters
GCDI’s GIS at Scale with Google Earth Engine introduces Earth Engine as a way to handle projects involving large geospatial datasets and highlights its ability to draw on a petabyte-scale database and cloud-based processing. Google’s own documentation says Earth Engine lets users run algorithms on georeferenced imagery and vectors stored on Google’s infrastructure, and that its public data catalog contains a large amount of publicly available imagery and vector datasets.

Key ideas

• Not every mapping project belongs on a laptop.
• Earth Engine is designed for scale, especially imagery and time series.
• It is code-based and more advanced than your earlier tool lessons.
• It belongs later in the pathway, not at the beginning.

Lesson
This lesson should position Earth Engine as a “next step” tool rather than a first mapping platform. It is best for learners whose questions require satellite imagery, long time spans, large geographic extents, or cloud-based analysis. That makes it very different from StoryMaps or QGIS, but also a useful reminder that “building maps” includes radically different computational scales.

This is also a nice moment to reinforce one of the core ideas of your site: tools should follow questions. If the question is neighborhood storytelling, Earth Engine is probably overkill. If the question is land-cover change or environmental time series, it may be exactly the right tool.

Example Project or GCDI resource
Use GIS at Scale with Google Earth Engine as the GCDI anchor. It explains the platform in approachable terms and gives concrete examples of why someone might need cloud-scale geospatial processing.

Open reading / resource
Use Google’s Get Started with Earth Engine guide. It introduces the JavaScript API, Earth Engine’s data model, and the public data catalog in a way that works well for advanced beginners.

Reflection / mini activity
Think of one mapping question that would be difficult to answer with a small local dataset but possible with satellite or time-series data. Write 4–5 sentences explaining why a cloud-scale platform might help.