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Cross-City Latent Space Alignment for Consistency Region Embedding

Chen, Meng ; Jia, Hongwei ; Li, Zechen ; Jia, Wenzhen ; Zhao, Kai ; Dai, Hongjun and Huang, Weiming LU (2025) 42nd International Conference on Machine Learning, ICML 2025 267. p.8261-8274
Abstract

Learning urban region embeddings has substantially advanced urban analysis, but their typical focus on individual cities leads to disparate embedding spaces, hindering cross-city knowledge transfer and the reuse of downstream task predictors. To tackle this issue, we present Consistency Region Embedding (CoRE), a unified framework integrating region embedding learning with crosscity latent space alignment. CoRE first embeds regions from two cities into separate latent spaces, followed by the alignment of latent space manifolds and fine-grained individual regions from both cities. This ensures compatible and comparable embeddings within aligned latent spaces, enabling predictions of various socioeconomic indicators without ground truth... (More)

Learning urban region embeddings has substantially advanced urban analysis, but their typical focus on individual cities leads to disparate embedding spaces, hindering cross-city knowledge transfer and the reuse of downstream task predictors. To tackle this issue, we present Consistency Region Embedding (CoRE), a unified framework integrating region embedding learning with crosscity latent space alignment. CoRE first embeds regions from two cities into separate latent spaces, followed by the alignment of latent space manifolds and fine-grained individual regions from both cities. This ensures compatible and comparable embeddings within aligned latent spaces, enabling predictions of various socioeconomic indicators without ground truth labels by migrating knowledge from label-rich cities. Extensive experiments show CoRE outperforms competitive baselines, confirming its effectiveness for crosscity knowledge transfer via aligned latent spaces.

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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of Machine Learning Research
volume
267
pages
14 pages
publisher
ML Research Press
conference name
42nd International Conference on Machine Learning, ICML 2025
conference location
Vancouver, Canada
conference dates
2025-07-13 - 2025-07-19
external identifiers
  • scopus:105023569398
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025 by the author(s).
id
7fe43dc7-df21-43db-96c0-4a76e2b47535
date added to LUP
2026-02-03 16:08:55
date last changed
2026-02-03 16:09:24
@inproceedings{7fe43dc7-df21-43db-96c0-4a76e2b47535,
  abstract     = {{<p>Learning urban region embeddings has substantially advanced urban analysis, but their typical focus on individual cities leads to disparate embedding spaces, hindering cross-city knowledge transfer and the reuse of downstream task predictors. To tackle this issue, we present Consistency Region Embedding (CoRE), a unified framework integrating region embedding learning with crosscity latent space alignment. CoRE first embeds regions from two cities into separate latent spaces, followed by the alignment of latent space manifolds and fine-grained individual regions from both cities. This ensures compatible and comparable embeddings within aligned latent spaces, enabling predictions of various socioeconomic indicators without ground truth labels by migrating knowledge from label-rich cities. Extensive experiments show CoRE outperforms competitive baselines, confirming its effectiveness for crosscity knowledge transfer via aligned latent spaces.</p>}},
  author       = {{Chen, Meng and Jia, Hongwei and Li, Zechen and Jia, Wenzhen and Zhao, Kai and Dai, Hongjun and Huang, Weiming}},
  booktitle    = {{Proceedings of Machine Learning Research}},
  language     = {{eng}},
  pages        = {{8261--8274}},
  publisher    = {{ML Research Press}},
  title        = {{Cross-City Latent Space Alignment for Consistency Region Embedding}},
  volume       = {{267}},
  year         = {{2025}},
}