Geospatial Information Systems are used by researchers and Humanitarian
Assistance and Disaster Response (HADR) practitioners to support a wide variety
of important applications. However, collaboration between these actors is
difficult due to the heterogeneous nature of geospatial data modalities (e.g.,
multi-spectral images of various resolutions, timeseries, weather data) and
diversity of tasks (e.g., regression of human activity indicators or detecting
forest fires). In this work, we present a roadmap towards the construction of a
general-purpose neural architecture (GPNA) with a geospatial inductive bias,
pre-trained on large amounts of unlabelled earth observation data in a
self-supervised manner. We envision how such a model may facilitate cooperation
between members of the community. We show preliminary results on the first step
of the roadmap, where we instantiate an architecture that can process a wide
variety of geospatial data modalities and demonstrate that it can achieve
competitive performance with domain-specific architectures on tasks relating to
the U.N.'s Sustainable Development Goals.Comment: Presented at AI + HADR Workshop at NeurIPS 202