  State-of-the-art deep learning methods have shown a remarkable capacity to
model complex data domains, but struggle with geospatial data. In this paper,
we introduce SpaceGAN, a novel generative model for geospatial domains that
learns neighbourhood structures through spatial conditioning. We propose to
enhance spatial representation beyond mere spatial coordinates, by conditioning
each data point on feature vectors of its spatial neighbours, thus allowing for
a more flexible representation of the spatial structure. To overcome issues of
training convergence, we employ a metric capturing the loss in local spatial
autocorrelation between real and generated data as stopping criterion for
SpaceGAN parametrization. This way, we ensure that the generator produces
synthetic samples faithful to the spatial patterns observed in the input.
SpaceGAN is successfully applied for data augmentation and outperforms compared
to other methods of synthetic spatial data generation. Finally, we propose an
ensemble learning framework for the geospatial domain, taking augmented
SpaceGAN samples as training data for a set of ensemble learners. We
empirically show the superiority of this approach over conventional ensemble
learning approaches and rivaling spatial data augmentation methods, using
synthetic and real-world prediction tasks. Our findings suggest that SpaceGAN
can be used as a tool for (1) artificially inflating sparse geospatial data and
(2) improving generalization of geospatial models.
