CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities
Urban representation learning encodes complex urban environments into general-purpose embeddings for diverse downstream tasks and emerging urban foundation models. However, current evaluations are limited, typically focusing on one or two cities and tasks and relying on random splits that introduce spatial leakage, leading to inflated performance and weak support for cross-location generalization and fair comparison. To address this, we propose CityRep, a unified benchmark that evaluates urban representations across data modalities, cities, and tasks using spatially structured splits. CityRep consists of three key components: (1) a spatial unit-agnostic evaluation framework that supports ...
| Authors | Junyuan Liu, Xinglei Wang, Zichao Zeng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 25 May 2026 |
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