spatial-cross-validation

The blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments. See

Spatial cross-validation for GeoAI

Draw map boundaries your data supports instead of inheriting ones that don't fit. spatialkit tessellates point observations into Voronoi, hex, grid or Delaunay cells, aggregates to them with autocorrelation-aware standard errors, and fits GWR, Bayesian GP or random-forest models validated by spatial cross-validation.

Data and code for pixel-based landslide susceptibility modelling in Huancabamba (Peru) using Random Forest and spatial block cross-validation.

Spatially validated machine-learning workflow for mineral prospectivity mapping using geochemistry, structural geology, magnetics and gravity.

BathySurrogate: An Open-Source Environmental Surrogate Framework for Satellite-Derived Bathymetry via Multi-Source Data Fusion and Spatial Validation

Classificação de uso e ocupação do solo (LULC) em Vieira do Minho, Portugal: Sentinel-1/2, LiDAR e Random Forest, com validação cruzada espacial e auditoria metodológica documentada. Portfólio técnico.

MSc dissertation workflow for predicting Leeds neighbourhood deprivation from Google Street View imagery using spatial validation and explainable machine learning.

AI-driven spatiotemporal downscaling (30m) and Explainable AI modeling of Urban Heat Island (UHI) dynamics across Mumbai's 24 municipal wards using Google Earth Engine and 27-year IMD records.

Spatial cross-validated Random Forest software for reproducible landslide susceptibility modelling and mapping.

Spatially blocked cross-validation and error-adjusted area estimation for land-cover maps. numpy-only, scikit-learn compatible.

Comparación reproducible de modelos del precio del suelo urbano bajo validación aleatoria y espacial en Quito, Ecuador

Remote-sensing experiments on wildfire burn-severity mapping, cross-event transfer, and pre-fire prediction using Landsat, MTBS, and CanLaBS.

3D spatial random forests and RF-GLS for mineral resource estimation under spatial block cross-validation