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Torch Geopooling - The geospatial pooling library for PyTorch

The Torch Geopooling library is an extension for PyTorch library that provide extra layers for building geospatial neural networks.

Here is an example of how you can use modules from Torch Geopooling library to train neural networks predicting geospatial features:

Installation

The library is distributed as PyPI package, to install that package, execute the following command:

pip install torch_geopooling

You can use the torch_geopooling library for building neural networks with geospatial indexing. The interface of the provided modules is compatible with PyTorch library, including automatic gradient computation.

Documentation

The Torch Geopooling Documentation contains additional details on how to get started with this library as well a few examples of training neural networks that use geo-pooling modules.

Usage

The module provides adaptive and regular modules that implement decomposition of point coordinates in 2-dimensional space. Decomposition in this context implies separation of the space into rectangles (quads).

Adaptive modules are building the decomposition during the training, while for regular modules the decomposition should be computed beforehand. As a result, adaptive module builds sparse decomposition, while regular module builds dense (regular) decomposition.

Using adaptive decomposition module for EPSG:4326 coordinates:

import torch
from torch_geopooling.nn import AdaptiveQuadPool2d

# Create 5-feature vector for each node in a decomposition.
pool = AdaptiveQuadPool2d(5, (-180, -90, 360, 180), max_depth=12, capacity=10)
input = torch.DoubleTensor(1024, 2).uniform_(-90, 90)
output = pool(input)

Using regular decomposition module for arbitrary polygon:

import torch
from shapely import Polygon
from torch_geopooling.nn import QuadPool2d

# Polygon for regular decomposition should be within an exterior boundary.
poly = Polygon([(0.0, 0.0), (10.0, 0.0), (10.0, 10.0), (0.0, 10.0)])
exterior = (-100.0, -100.0, 200.0, 200.0)
# Create 3-feature vector for each node in a decomposition.
pool = QuadPool2d(3, poly, exterior, max_depth=10)
input = torch.DoubleTensor(200, 2).uniform_(0.0, 10.0)
output = pool(input)

Using 2-dimensional embedding module for learning data on sphere:

import torch
from torch_geopooling.nn import Embedding2d

embedding = Embedding2d((16, 16, 2), padding=(3, 3), exterior=(-100, 100, 200.0, 200.0))
input = torch.DoubleTensor(1024, 2).normal_(5.0, 1.0)
output = embedding(input)

License

The Torch Geopooling is distributed under GPLv3 license. See the LICENSE file for full license text.