  In this paper, we evaluate the accuracy of deep learning approaches on
geospatial vector geometry classification tasks. The purpose of this evaluation
is to investigate the ability of deep learning models to learn from geometry
coordinates directly. Previous machine learning research applied to geospatial
polygon data did not use geometries directly, but derived properties thereof.
These are produced by way of extracting geometry properties such as Fourier
descriptors. Instead, our introduced deep neural net architectures are able to
learn on sequences of coordinates mapped directly from polygons. In three
classification tasks we show that the deep learning architectures are
competitive with common learning algorithms that require extracted features.
