城市表示学习领域终于有了一个靠谱的评估标准——CityRep 解决了空间泄漏和跨城市泛化评估的痛点，做城市计算或地理空间 AI 的研究者可以直接用这个基准来检验自己的模型，避免被随机划分的虚假高分误导。
城市表示学习将复杂城市环境编码为通用嵌入，但现有评估多局限于少数城市和任务，且随机划分导致空间泄漏，高估性能。CityRep 提出统一基准，包含空间单元无关的评估框架、基于区块的空间划分协议，以及覆盖 8 城市 8 任务的可扩展套件。评估 11 个模型发现，随机划分会扭曲性能排名，且模型表现因城市和任务差异显著。该基准提供数据集、评估管道和诊断工具，旨在推动城市表示学习的公平比较和泛化能力研究。
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 heterogeneous urban representations through a standardized alignment module; (2) a unified evaluation protocol using block-based spatial splits to mitigate spatial leakage and enable rigorous model comparison; and (3) an extensible multi-city, multi-task benchmark suite spanning 8 cities and 8 tasks across regression, classification, and distribution prediction. We evaluate 11 representative urban representation models. Results show that performance is highly sensitive to the split protocol, with random splits inflating scores and altering model rankings. We also observe substantial variability across cities and tasks, underscoring the need for generalization-aware evaluation. CityRep is released as a reproducible benchmark with datasets, evaluation pipelines, and diagnostic tools to facilitate fair comparison and support future research in urban representation learning towards urban foundation models.