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.

翻译：预训练基础模型（PFMs）因其能够学习通用表征并直接应用于广泛下游任务的能力，已在人工智能领域引发范式转变。尽管PFMs在自然语言处理与计算机视觉等领域的应用已取得重大突破，但其在处理地理空间数据和解答城市问题方面的能力仍存在局限。这种局限性源于地理空间数据的内在异质性——这不仅涵盖点、线段和区域等不同类型的数据，还涉及空间位置、视觉特征和文本标注等多模态信息。志愿者地理信息项目的蓬勃发展，以及开放地理空间数据源（如全球免费访问的OpenStreetMap）的日益普及，为弥合这一差距展现了崭新机遇。本文提出CityFM框架，这是一种自监督学习框架，旨在选定地理区域（如城市）内训练基础模型。CityFM完全依赖OSM的开放数据，通过融合空间、视觉和文本信息，为不同实体类型生成多模态表征。我们从定性角度分析基于该基础模型生成的实体表征，并针对道路、建筑物和区域级下游任务开展定量实验，将其结果与各专项任务定制算法进行对比。所有实验表明，CityFM的性能均优于或持平于基准方法。