Spatial machine learning
Features, spatial cross-validation, prediction rasters and the area a model can honestly speak for.
Everything on Spatial Workflow about spatial machine learning, in the order that usually works: understand the idea, then run the task, then fix it when it goes wrong. 15 guides in total.
2 learning paths run through this topic — follow one if you would rather work through a route than pick a single guide.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
- Spatial Machine Learning Explained: Why Location Breaks the Usual Rules spatial machine learning explained
- Spatial Leakage Explained: Why Random Cross-Validation Lies spatial leakage explained
- Spatial Features Explained: Turning Geometry into Columns spatial features explained
- Extrapolation in Space Explained: The Area of Applicability area of applicability explained
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to Classify Land Cover from Satellite Imagery in Python classify land cover python
- How to Sample Training Points from Rasters and Polygons sample training points python
- How to Build Spatial Features for a Machine Learning Model build spatial features python
- How to Do Spatial Cross-Validation in Python spatial cross validation python
- How to Predict a Continuous Surface with Random Forest random forest spatial prediction python
- How to Turn Model Predictions Back into a Raster predictions to raster python
- How to Evaluate a Spatial Model Honestly evaluate spatial model python
- How to Handle Class Imbalance in a Spatial Classifier class imbalance spatial classifier
🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
Learning paths through this topic
A curated route rather than a list — the guides in the order that builds on itself.
Related topics
Subjects that keep coming up alongside this one, most overlap first.