GeoSurrogates.jl

Implicit Neural Representations for Geospatial Data

GeoSurrogates.jl creates surrogate models of geospatial data. It provides multiple approaches to learn continuous functions from spatial raster data, using both classical methods and neural networks to create compact, efficient representations of geographic phenomena.

Features

• Classical Surrogates -- Linear regression, inverse distance weighting, radial basis functions, thin plate splines, and B-spline interpolation
• Neural Network Surrogates -- SIREN (Sinusoidal Representation Networks) for terrain, wind fields, and categorical data
• Rasters.jl Integration -- Works directly with Raster and RasterStack objects
• Arbitrary Resolution -- Predict at any resolution, not just the training grid
• Memory Efficient -- Neural networks as compact alternatives to storing full rasters
• Composable -- AdditiveModel for boosting-style ensembles of surrogates

Installation

using Pkg
Pkg.add("GeoSurrogates")

Quick Example

using GeoSurrogates, Rasters

# Load a raster
elev = Raster("path/to/elevation.tif")

# Create a simple interpolation-based surrogate
surrogate = RasterWrap(elev)

# Predict at any coordinate
predict(surrogate, (-105.5, 40.2))

# Or create a neural network surrogate for compression
model = ImplicitTerrain.Model()
fit!(model, normalize(elev); steps=1000)

# Predict on a new raster grid
predicted = predict(model, new_raster)

Surrogate Types

Type	Description	Use Case
RasterWrap	B-spline interpolation	Fast exact interpolation
LinReg	Linear regression	Simple trend modeling
IDW	Inverse distance weighting	Scattered data interpolation
RBF	Radial basis functions	Flexible kernel interpolation
TPS	Thin plate splines	Smooth surface fitting
GeomWrap	Distance-based kernel	Geometry influence fields
CategoricalRasterWrap	Kernel smoothing	Categorical raster data
ImplicitTerrain.Model	Cascaded SIREN	Terrain compression
WindSurrogate.WindSIREN	SIREN for vectors	Wind field modeling
CatSIREN.CatSIREN	SIREN with softmax	Categorical classification
AdditiveModel	Boosted ensemble	Composing multiple surrogates

References

• Sitzmann et al. Implicit Neural Representations with Periodic Activation Functions (2020)
• ImplicitTerrain: https://arxiv.org/abs/2406.00227