30-Day Map Challenge 2025: Director’s Cut

Build 30 Real-World Maps in Python Using Open Data, Satellite Imagery & GeoAI

What you’ll learn
✓ Build 30 complete, reproducible geospatial maps in Python using real-world open datasets — from points and polygons to satellite imagery and 3D surfaces. Maste
✓ Master the core Python geospatial stack including GeoPandas, Folium, Plotly, PyDeck, Rasterio, osmnx, Pandana, and Datashader across diverse project types.
✓ Process and visualize satellite and raster data from ESA Sentinel, NASA, NOAA VIIRS, and multispectral sensors to detect wildfires, map nighttime lights, and cl
✓ Build network and graph-based maps including river connectivity algorithms, OSM routing networks, and chemical similarity networks visualized in Gephi.
✓ Create time-lapse animations and temporal visualizations showing urban growth, population change, and speculative future projections using GHSL, WorldPop, and N
✓ Design 3D elevation and surface maps from LiDAR, bathymetry, lunar DEM, and hexagonal grid data using Plotly and PyDeck.
✓ Develop a personal mapping workflow — from data acquisition and processing to visualization and storytelling — that you can apply to any city, dataset, or resea

Requirements
● Basic Python programming experience is required — you should be comfortable with loops, functions, and working with libraries like Pandas and Matplotlib.
● Familiarity with fundamental GIS concepts is helpful but not essential — if you know what a shapefile, a coordinate system, or a point layer is, you’re in good shape.
● No prior geospatial Python experience is needed — we introduce every library as we use it, and all notebooks run out of the box with the included data samples.
● A working Python environment with conda or pip — setup instructions and a full requirements file are provided in the first section of the course.

Description
The 30-Day Map Challenge is one of the most creative events in the geospatial community — 30 days, 30 themes, 30 maps. This is the Director’s Cut.

Every November, thousands of mapmakers around the world take on the challenge. This course packages the complete 2025 edition into a structured learning experience — with source code, ready-to-run data samples, behind-the-scenes content, and 30 tutorial videos organized into 8 thematic sections.

What makes this the Director’s Cut? All 30 tutorial videos are freely available on YouTube. What you’re getting here is the structured course experience — thematically grouped, with section bridges that connect the projects into a coherent learning arc — plus every notebook, every dataset, and the behind-the-scenes video showing how 30 maps in 30 days actually gets planned and executed.

What you’ll build: From interactive POI maps and wildfire damage detection pipelines to 3D lunar surface models and food chemistry similarity networks — these are complete, real-world projects built entirely in Python on open data. No point-and-click software. No toy datasets.

How the course is structured: The 30 projects are grouped into 8 thematic sections — Vector Foundations, Urban Analytics & Accessibility, Time Change & Animation, Raster & Remote Sensing, 3D & Surfaces, Networks & Graphs, Creative & Experimental, and GeoAI Preview. Each section opens with a short bridge video framing what connects the projects and what you’ll take away.

The Python stack: GeoPandas · Folium · Plotly · PyDeck · Rasterio · osmnx · Pandana · Datashader · scikit-learn · NetworkX · H3 · OpenCV · Gephi

The data sources: OpenStreetMap · Natural Earth · NASA · ESA Sentinel · NOAA VIIRS · WorldPop · GHSL · GTFS · IUCN · Google AlphaEarth · Wyvern · GLOBathy · and more

Who this is for: This course is for intermediate Python users who want to build a serious geospatial portfolio — whether you’re coming from data science, urban planning, geography, or remote sensing. If you’ve been following the challenge on YouTube or Substack and want to go deeper, this is built for you.

Who this course is for
■ Python developers and data scientists who want to add geospatial visualization to their skill set through 30 real, complete, and reproducible projects.
■ GIS professionals and urban analysts looking to transition from point-and-click tools like ArcGIS or QGIS to a fully code-based Python workflow.
■ Followers of the #30DayMapChallenge community who want to go deeper than the free YouTube tutorials — with source code, ready-to-run data, and behind-the-scenes context.
■ Students and researchers in geography, urban planning, environmental science, or data science who need practical, portfolio-ready mapping projects built on open data.

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