This module introduces learners to how color choices influence interpretation, accuracy, and trust in maps. Learners explore sequential, diverging, and categorical color schemes and practice selecting palettes that match data types and communication goals.

Learners break down the anatomy of a professional map and learn where to place titles, legends, scale bars, north arrows, and sources so the map reads clearly without distraction.

This module develops learners’ ability to evaluate maps critically. Learners learn how contrast, alignment, size, and spacing guide the viewer’s attention and practice diagnosing visual problems in real examples.

You are introduced to Folium and explore how choropleth maps communicate geographic patterns in data. Using COVID case data, learners build their first interactive choropleth map and understand how data values are translated into visual gradients.

You will learn to enhance your maps by adding interactive features that allow users to explore data dynamically. This module focuses on usability, clarity, and stakeholder-friendly design.

This module completes the workflow by moving maps from a local environment to the web. You will publish your interactive maps online so they can be shared with non-technical audiences.

In this module, you will explore how satellite data can be used to understand vegetation change and why NDVI is a widely trusted indicator in environmental analysis. You will begin by reflecting on how NDVI trends support real-world environmental reporting, then get oriented to Google Earth Engine as a platform for working with large datasets. Through guided examples, you will write your first Earth Engine script to load and visualize the MODIS NDVI collection. By the end of the module, you will be comfortable navigating Earth Engine and working with NDVI data as a foundation for deeper analysis.

In this module, you will learn how to transform raw NDVI time-series data into summaries that are easier to interpret and communicate. You will reflect on why raw satellite imagery can be difficult to use in reports and explore how temporal reduction helps reveal meaningful patterns. By the end of the module, you will be able to create annual median NDVI datasets that are ready for trend analysis and visualization.

This module focuses on turning summarized NDVI data into insights that others can understand and act on. You will reflect on why charts play a critical role in environmental communication and learn how to create NDVI time-series charts in Google Earth Engine. By the end of the module, you will interpret NDVI trends and evaluate whether your chart supports a clear, defensible conclusion for an environmental report.

In this module, you will use generative AI to enhance geospatial workflows across cartography, web mapping, and cloud GIS. You will learn how AI can generate code, troubleshoot errors, and improve map design while requiring validation for accuracy. You will practice writing effective prompts with spatial context to generate and refine Folium and Earth Engine outputs. Through a hands-on activity, you will apply AI to build, improve, and troubleshoot a mapping workflow. You will also evaluate AI-generated results to determine when they are reliable and when human judgment is needed.

In this project, you will create an interactive vegetation dashboard that combines Google Earth Engine analysis with a Folium web map. You will calculate current and baseline NDVI conditions, summarize anomaly values by conservation area, and turn the results into a public-facing map. You will design the dashboard using clear cartographic choices, add interactive popups and layer controls, and publish the final map online. This project shows how cloud GIS, web mapping, and cartographic design work together in real-world geospatial communication.