  That most deep learning models are purely data driven is both a strength and
a weakness. Given sufficient training data, the optimal model for a particular
problem can be learned. However, this is usually not the case and so instead
the model is either learned from scratch from a limited amount of training data
or pre-trained on a different problem and then fine-tuned. Both of these
situations are potentially suboptimal and limit the generalizability of the
model. Inspired by this, we investigate methods to inform or guide deep
learning models for geospatial image analysis to increase their performance
when a limited amount of training data is available or when they are applied to
scenarios other than which they were trained on. In particular, we exploit the
fact that there are certain fundamental rules as to how things are distributed
on the surface of the Earth and these rules do not vary substantially between
locations. Based on this, we develop a novel feature pooling method for
convolutional neural networks using Getis-Ord Gi* analysis from geostatistics.
Experimental results show our proposed pooling function has significantly
better generalization performance compared to a standard data-driven approach
when applied to overhead image segmentation.
