arXiv AI By Sean Bin Yang, Ying Sun, Zongyi Xu, Tung Kieu, Jilin Hu, Bin Yang, Kristian Torp, Hua Lu, Torben Bach Pedersen

When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning

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arXiv AI
Oct 26

Rethinking Multi-View Fusion for Urban Representation Learning

The paper "Rethinking Multi-View Fusion for Urban Representation Learning" introduces PRISM, a framework that addresses two key issues in urban data fusion: varying modality utility across regions and persistent gradient imbalance among heterogeneous objectives. PRISM employs sparse Mixture-of-Experts routing for region-specific view allocation and a PID-inspired regulation mechanism to reduce gradient conflicts during training. Experiments on three large-scale urban datasets demonstrate that PRISM achieves competitive performance while offering more stable optimization and balanced region representations.

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Synergistic Fusion of Topological Structure and Temporal Semantics of Mobility for Urban Region Embedding

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arXiv Machine Learning
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A multi-view contrastive learning framework for spatial embeddings in risk modelling

The paper introduces a multi‑view contrastive learning framework that creates low‑dimensional spatial embeddings by combining satellite imagery and OpenStreetMap data across Europe. These embeddings align with coordinate‑based encodings, allowing any dataset with latitude‑longitude pairs to be enriched with meaningful spatial features without needing the original spatial inputs. In case studies on French real‑estate prices and Belgian flood claim counts, models using the embeddings outperform those using raw coordinates, improving predictive accuracy and territorial risk classification while offering explainable spatial effects.

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