UrbanFusion: Stochastic Multimodal Fusion for Contrastive Learning of Robust Spatial Representations
arXiv:2510.13774v2 Announce Type: replace Abstract: 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 generic models for spatial representations often support only limited modalities and lack multimodal fusion capabilities. To overcome these challenges, we present UrbanFusion, a spatial representation model that features Stochastic Multimodal Fusion (SMF). The framework employs modality-specific encoders to process different types of
The proliferation of diverse geospatial data and the increasing demand for predictive urban analytics are driving the need for more robust and integrated AI models.
Advanced spatial representation models like UrbanFusion can significantly improve forecasting of critical urban phenomena, impacting policy, investment, and public services.
The ability to integrate and learn from various geospatial data types through stochastic multimodal fusion will lead to more accurate and generalizable urban AI applications.
- · Smart city developers
- · Urban planners
- · Real estate analytics firms
- · Public health organizations
- · Task-specific spatial modeling approaches
- · Models reliant on single data modalities
Improved accuracy in forecasting housing prices, public health indicators, and resource allocation within urban environments.
Development of more comprehensive and autonomously managed smart city infrastructure, relying on better predictive capabilities.
Enhanced resilience and efficiency of urban systems, potentially leading to more sustainable and equitable cities powered by sophisticated AI.
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