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

Augmenting correlation structures in spatial data using deep generative models