Forecasting urban phenomena such as housing prices and public health indicators requires the effective integration of various geospatial data. Current methods primarily utilize task-specific models, while recent foundation models for spatial representations often support only limited modalities and lack multimodal fusion capabilities. To overcome these challenges, we present UrbanFusion, a Geo-Foundation Model (GeoFM) that features Stochastic Multimodal Fusion (SMF). The framework employs modality-specific encoders to process different types of inputs, including street view imagery, remote sensing data, cartographic maps, and points of interest (POIs) data. These multimodal inputs are integrated via a Transformer-based fusion module that learns unified representations. An extensive evaluation across 41 tasks in 56 cities worldwide demonstrates UrbanFusion's strong generalization and predictive performance compared to state-of-the-art GeoAI models. Specifically, it 1) outperforms prior foundation models on location-encoding, 2) allows multimodal input during inference, and 3) generalizes well to regions unseen during training. UrbanFusion can flexibly utilize any subset of available modalities for a given location during both pretraining and inference, enabling broad applicability across diverse data availability scenarios. All source code is available at https://github.com/DominikM198/UrbanFusion.

翻译：预测住房价格和公共卫生指标等城市现象，需要有效整合各类地理空间数据。现有方法主要采用任务专用模型，而近期兴起的空间表征基础模型通常仅支持有限模态且缺乏多模态融合能力。为应对这些挑战，我们提出UrbanFusion——一个具备随机多模态融合特性的地理基础模型。该框架采用模态专用编码器处理街景图像、遥感数据、地图矢量和兴趣点数据等不同类型的输入，并通过基于Transformer的融合模块整合这些多模态输入以学习统一表征。在全球56个城市41项任务上的广泛评估表明，相较于最先进的地理人工智能模型，UrbanFusion展现出优异的泛化能力和预测性能。具体而言，该模型：1）在位置编码任务上超越现有基础模型；2）支持推理阶段的多模态输入；3）对训练阶段未见的区域具有良好的泛化能力。UrbanFusion在预训练和推理阶段均可灵活利用任意可用模态子集，从而在多样化的数据可用性场景中具备广泛适用性。全部源代码发布于https://github.com/DominikM198/UrbanFusion。