@inproceedings{xu-etal-2026-urban,
title = "{URBAN}-{PROTOS}: Discrete Semantic Prototypes for Cross-City Region Representation Learning",
author = "Xu, Zihang and
Feng, Xinshun and
Xie, Haozhi and
Qiao, Yi and
Han, Liangzhe and
Sun, Leilei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1373/",
pages = "29220--29235",
ISBN = "979-8-89176-462-0",
abstract = "Urban region representation learning plays a fundamental role in urban computing, providing compact embeddings that support a wide range of downstream analytical tasks. Existing methods are developed under a single-city setting and rely on continuous vector representations learned from city-specific spatiotemporal data. Learning transferable and semantically meaningful region representations in cross-city scenarios remains a significant challenge. In this paper, we propose URBAN-PROTOS, a two-stage framework for cross-city learning of urban region representations. We first abstract multi-city mobility data into discrete Urban Prototypes, which capture city-invariant spatiotemporal regularities from localized heterogeneous contexts. These prototypes serve as shared semantic units across cities and are further integrated into the semantic space of large language models through a prototype-guided alignment mechanism. Extensive experiments conducted demonstrate that URBAN-PROTOS consistently outperforms existing methods in cross-city transfer settings, while maintaining strong performance in single-city evaluations. Further analyses show that the learned region embeddings encode deep semantic regularities shared across cities, providing robust support for cross-city generalization."
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<abstract>Urban region representation learning plays a fundamental role in urban computing, providing compact embeddings that support a wide range of downstream analytical tasks. Existing methods are developed under a single-city setting and rely on continuous vector representations learned from city-specific spatiotemporal data. Learning transferable and semantically meaningful region representations in cross-city scenarios remains a significant challenge. In this paper, we propose URBAN-PROTOS, a two-stage framework for cross-city learning of urban region representations. We first abstract multi-city mobility data into discrete Urban Prototypes, which capture city-invariant spatiotemporal regularities from localized heterogeneous contexts. These prototypes serve as shared semantic units across cities and are further integrated into the semantic space of large language models through a prototype-guided alignment mechanism. Extensive experiments conducted demonstrate that URBAN-PROTOS consistently outperforms existing methods in cross-city transfer settings, while maintaining strong performance in single-city evaluations. Further analyses show that the learned region embeddings encode deep semantic regularities shared across cities, providing robust support for cross-city generalization.</abstract>
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%0 Conference Proceedings
%T URBAN-PROTOS: Discrete Semantic Prototypes for Cross-City Region Representation Learning
%A Xu, Zihang
%A Feng, Xinshun
%A Xie, Haozhi
%A Qiao, Yi
%A Han, Liangzhe
%A Sun, Leilei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-urban
%X Urban region representation learning plays a fundamental role in urban computing, providing compact embeddings that support a wide range of downstream analytical tasks. Existing methods are developed under a single-city setting and rely on continuous vector representations learned from city-specific spatiotemporal data. Learning transferable and semantically meaningful region representations in cross-city scenarios remains a significant challenge. In this paper, we propose URBAN-PROTOS, a two-stage framework for cross-city learning of urban region representations. We first abstract multi-city mobility data into discrete Urban Prototypes, which capture city-invariant spatiotemporal regularities from localized heterogeneous contexts. These prototypes serve as shared semantic units across cities and are further integrated into the semantic space of large language models through a prototype-guided alignment mechanism. Extensive experiments conducted demonstrate that URBAN-PROTOS consistently outperforms existing methods in cross-city transfer settings, while maintaining strong performance in single-city evaluations. Further analyses show that the learned region embeddings encode deep semantic regularities shared across cities, providing robust support for cross-city generalization.
%U https://aclanthology.org/2026.findings-emnlp.1373/
%P 29220-29235
Markdown (Informal)
[URBAN-PROTOS: Discrete Semantic Prototypes for Cross-City Region Representation Learning](https://aclanthology.org/2026.findings-emnlp.1373/) (Xu et al., Findings 2026)
ACL