Pre-trained Foundation Models (PFMs) have ushered in a paradigm-shift in
Artificial Intelligence, due to their ability to learn general-purpose
representations that can be readily employed in a wide range of downstream
tasks. While PFMs have been successfully adopted in various fields such as
Natural Language Processing and Computer Vision, their capacity in handling
geospatial data and answering urban questions remains limited. This can be
attributed to the intrinsic heterogeneity of geospatial data, which encompasses
different data types, including points, segments and regions, as well as
multiple information modalities, such as a spatial position, visual
characteristics and textual annotations. The proliferation of Volunteered
Geographic Information initiatives, and the ever-increasing availability of
open geospatial data sources, like OpenStreetMap, which is freely accessible
globally, unveil a promising opportunity to bridge this gap. In this paper, we
present CityFM, a self-supervised framework to train a foundation model within
a selected geographical area of interest, such as a city. CityFM relies solely
on open data from OSM, and produces multimodal representations of entities of
different types, incorporating spatial, visual, and textual information. We
analyse the entity representations generated using our foundation models from a
qualitative perspective, and conduct quantitative experiments on road,
building, and region-level downstream tasks. We compare its results to
algorithms tailored specifically for the respective applications. In all the
experiments, CityFM achieves performance superior to, or on par with, the
baselines.

通过开放地理空间数据和自我监督学习，CityFM 构建了一个多模态的预训练基础模型，用于处理地理空间数据和回答城市相关问题，并在道路、建筑和区域级别的任务上获得了优秀的性能。