geospatial-ml

name	geospatial-ml
description	Use when applying machine learning to geospatial data, including spatial feature engineering, morphometric indicators, urban analysis, spatial cross-validation, library selection, or integrating geopandas, momepy, pysal, osmnx, torchgeo, or rasterio into an ML pipeline.

Geospatial ML

Use this skill when the task involves building or evaluating a machine learning pipeline that consumes geospatial data as input, or when spatial structure must be respected during modelling.

Library Landscape

Vector and geometry

Library	Role
geopandas	GeoDataFrame with CRS awareness; primary table for vector ML features
shapely	Geometry primitives and operations behind geopandas
pyproj	CRS and coordinate transforms
osmnx	Download and analyse OpenStreetMap road and place networks
momepy	Urban morphometrics from building footprints, street networks, and city blocks
pysal / libpysal	Spatial weights matrices, spatial statistics
esda	Moran's I, local spatial autocorrelation (LISA), spatial clustering
mgwr	Multiscale Geographically Weighted Regression
spopt	Spatial optimisation (p-median, regionalisation)
h3 / h3-pandas	Uber Hexagonal Hierarchical Spatial Index for grid aggregation

Raster and remote sensing

Library	Role
rasterio	Read/write rasters, window reads, masking
xarray / rioxarray	N-dimensional arrays with spatial metadata and lazy loading
numpy	Array arithmetic for raster band math
torchgeo	PyTorch datasets, samplers, and transforms for geospatial rasters
segmentation-models-pytorch	Pre-built encoder-decoder architectures for semantic segmentation

Spatial ML and statistics

Library	Role
scikit-learn	Standard ML; works with geopandas DataFrames
mlxtend	Spatial feature selection helpers
lightgbm / xgboost	Gradient boosting; handles spatial tabular features well
pytorch / lightning	Deep learning backbone for point clouds, rasters, graphs
torch-geometric	Graph neural networks over spatial neighbourhood graphs

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Workflow

1. Define the target and geometry

• What is being predicted? (building type, land cover class, property value, urban density, flood risk)
• What geometry type holds labels? (point, polygon, pixel, hexagon)
• Is the label spatially continuous or discrete?

2. Construct spatial features

From building footprints / urban blocks (momepy)

• momepy.Area, momepy.Perimeter, momepy.CircularCompactness, momepy.Elongation
• momepy.CoveredArea, momepy.NeighboringStreetOrientationDeviation
• momepy.BlocksCount, momepy.Reached — network-based reach metrics
• Build a queen/rook libpysal.weights.Queen tessellation before computing context metrics

From street networks (osmnx)

• Download with osmnx.graph_from_place or bounding box
• Compute betweenness, closeness, edge density
• osmnx.stats.basic_stats for global network measures
• Project graph with osmnx.project_graph before any distance-dependent metric

From vector attributes (geopandas)

• Spatial join (gpd.sjoin) to attach context from neighbouring layers
• Buffer + dissolve for density ring features
• Distance to nearest feature with GeoSeries.distance
• H3 aggregation for grid-level summary features

From raster bands (rasterio / xarray)

• Zonal statistics: mean, std, median per polygon with rasterstats.zonal_stats
• Band indices: NDVI (NIR - Red) / (NIR + Red), NDWI, NDBI
• Texture features from windowed reads (GLCM via skimage.feature.graycomatrix)

3. Assemble the feature matrix

• Merge all features into a single GeoDataFrame keyed on a stable geometry ID
• Drop geometry column before passing to sklearn estimators: df.drop(columns='geometry')
• Check for infinity, extreme outliers, and CRS-dependent features computed in wrong units

4. Handle spatial autocorrelation

• Compute Moran's I on the target variable (esda.Moran) before modelling
• If Moran's I is significantly positive, standard random splits will leak spatial signal
• Use spatial cross-validation to avoid this (see section below)

5. Spatial cross-validation

• Block CV (sklearn-spatial or manual): divide study area into geographic blocks, treat each block as a fold
• Buffered leave-one-out (BLOO): exclude neighbours within a distance buffer from training
• H3 tile CV: assign H3 cell as fold key; shuffle cells, not rows
• Never use train_test_split with shuffle=True when geometry exhibits autocorrelation

6. Train and evaluate

• Baseline: spatial naive model (predict nearest-neighbour label or spatial lag mean)
• Compare model RMSE / F1 on spatial CV folds vs random CV folds — large gap indicates leakage
• Feature importance: SHAP values highlight which spatial indicators drive predictions
• Map residuals back onto geometry to diagnose spatial bias

7. Raster / deep learning path (torchgeo)

• Use torchgeo.datasets for standard benchmarks (LandCover.AI, EuroSAT, SpaceNet)
• torchgeo.samplers.RandomGeoSampler for patch-based training
• torchgeo.transforms for normalisation, augmentation respecting CRS
• Store predictions as GeoTIFF with rasterio.open(..., 'w') and matching transform + crs

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Common Pitfalls

Pitfall	Risk	Fix
Random train/test split on autocorrelated data	Inflated metrics; model fails in new areas	Block or buffered spatial CV
momepy metrics before CRS projection	Wrong metric units (degrees not meters)	Always project to a local CRS first
Mixing CRS across joined layers	Geometry misalignment	Reproject all layers to one CRS before any join
Including coordinates as raw ML features	Model memorises study-area location	Use derived features (distance, density) not raw lat/lon
Nodata as a valid pixel value	Corrupts raster statistics	Confirm nodata mask before band math
Graph distance in angular CRS	Incorrect network centrality	Project graph before computing metrics

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Output Format

When reporting on a geospatial ML pipeline:

Target variable:
Geometry type and source:
Feature groups constructed:
  - Morphometrics:
  - Network:
  - Raster / remote sensing:
  - Context / aggregated:
Spatial autocorrelation check (Moran's I):
CV strategy:
Baseline model and metric:
Candidate model and metric:
Spatial bias in residuals:
Recommended next step:

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Guidelines

• Always project data to a metric CRS before computing any distance, area, or density feature.
• Treat spatial autocorrelation as a data leakage problem, not a curiosity.
• Keep geometry and feature matrix in sync via a stable ID column; do not rely on row order.
• Prefer interpretable features (density, compactness, connectivity) over raw coordinates.
• Document which spatial scale each feature operates at (parcel, block, street, neighbourhood).
• For raster segmentation, report IoU per class, not just mean accuracy.

Example Triggers

• "Build a feature set from building footprints using momepy for a land use classifier"
• "How do I do spatial cross-validation to avoid data leakage?"
• "Which Python libraries should I use for urban ML with OpenStreetMap data?"
• "Compute street network centrality features for a regression model"
• "Train a semantic segmentation model on satellite imagery with torchgeo"
• "I have geopandas polygons and want to feed them into scikit-learn — what features should I extract?"
• "How do I check for and handle spatial autocorrelation before modelling?"