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Earth observation offers new insight into anthropogenic changes to nature,and how these changes are effecting (and are effected by) the built environmentand the real economy. With the global availability of medium-resolution(10-30m) synthetic aperture radar (SAR) Sentinel-1 and multispectral Sentinel-2imagery, machine learning can be employed to offer these insights at scale,unbiased to the reporting of companies and countries. In this paper, Iintroduce DeepSentinel, a data pipeline and experimentation framework forproducing general-purpose semantic embeddings of paired Sentinel-1 andSentinel-2 imagery. I document the development of an extensible corpus oflabelled and unlabelled imagery for the purposes of sensor fusion research.With this new dataset I develop a set of experiments applying popularself-supervision methods and encoder architectures to a land coverclassification problem. Tile2vec spatial encoding with a self-attention enabledResNet model outperforms deeper ResNet variants as well as pretraining withvariational autoencoding and contrastive loss. All supporting and derived dataand code are made publicly available.

Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model