A General Purpose Neural Architecture for Geospatial Systems

• URL: http://arxiv.org/abs/2211.02348v1
• Date: Fri, 4 Nov 2022 09:58:57 GMT
• Title: A General Purpose Neural Architecture for Geospatial Systems
• Authors: Nasim Rahaman and Martin Weiss and Frederik Tr\"auble and Francesco Locatello and Alexandre Lacoste and Yoshua Bengio and Chris Pal and Li Erran Li and Bernhard Sch\"olkopf
• Abstract summary: We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
• Score: 142.43454584836812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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