A General Purpose Neural Architecture for Geospatial Systems
This work addresses collaboration difficulties in geospatial systems for researchers and HADR practitioners, but it is incremental as it presents a roadmap with preliminary results.
The paper tackles the challenge of heterogeneous geospatial data and diverse tasks by proposing a roadmap for a general-purpose neural architecture with geospatial inductive bias, showing preliminary competitive performance on Sustainable Development Goals tasks.
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.