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.

本文介绍了一种通用的神经结构图，其具有地理空间归纳偏差，经过大量未标记的地球观测数据的自我监督训练。该模型可能有助于研究人员和人道主义援助和灾难响应从业者之间的合作。作者展示了这个神经结构图的初步结果，该神经结构图可以处理各种地理空间数据类型，并证明其在与领域特定的结构在联合国可持续发展目标相关任务上具有竞争性的表现。

一个通用的神经架构用于地理空间系统