UrbanWell: 面向时空城市福祉分析的多模态大语言模型基准测试
UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics
AI总结：
提出UrbanWell基准，通过卫星和街景图像联合建模，系统评估多模态大语言模型在环境、空间可达性、城市形态、活力和主观感知等5类城市福祉指标上的时空推理能力，并定义时序预测和趋势分类任务。
摘要：
从多模态数据理解城市福祉需要整合异构的时空信号，这对当前的多模态大语言模型（MLLMs）构成了重大挑战。我们提出了UrbanWell，一个大规模基准测试，旨在通过卫星和街景图像的联合建模，系统评估MLLMs在时空推理方面的能力，用于城市福祉分析。UrbanWell覆盖多个年份的38个城市，包含多样化的指标，涵盖（1）环境条件（CO$_2$、NO$_2$、PM${2.5}$和归一化植被指数），（2）空间可达性（到超市和餐馆的最小距离），（3）城市形态（道路长度、道路密度和土地利用），（4）城市活力（人口、经济活动多样性和土地利用多样性），以及（5）主观感知属性（例如安全性、美观性、活力、财富和宁静度）。所有指标在网格级别对齐，以实现标准化评估。除了静态预测，UrbanWell还定义了时序推理任务，包括基于历史观测的未来值预测和时序趋势分类。我们在零样本设置下对15个有代表性的最先进MLLMs进行了基准测试，提供了跨空间和时间维度的全面比较评估。实验结果表明，尽管MLLMs能够捕捉显著的空间和感知线索，但其性能在涵盖环境和主观感知的异质城市指标上差异显著。UrbanWell作为评估城市福祉分析中多模态时空推理的统一基准，为系统评估和未来多模态城市智能研究提供了标准化测试平台。我们的代码和数据集可通过https://github.com/axin1301/UrbanWell-Benchmark获取。
英文摘要：
Understanding urban wellbeing from multimodal data requires integrating heterogeneous spatial and temporal signals, posing significant challenges for current multimodal large language models (MLLMs). We introduce UrbanWell, a large-scale benchmark designed to systematically evaluate the spatio-temporal reasoning capabilities of MLLMs for urban wellbeing analytics through joint modeling of satellite and street view imagery. UrbanWell spans 38 cities across multiple years and includes diverse indicators covering (1) environmental conditions (CO$_2$, NO$_2$, PM${2.5}$, and Normalized Difference Vegetation Index), (2) spatial accessibility (minimum distance to supermarkets and restaurants), (3) urban form (road length, road density, and land use), (4) urban vitality (population, economic activity diversity, and land use diversity), and (5) subjective perception attributes (e.g., safety, beauty, liveliness, wealth, and quietness). All indicators are aligned at grid level to enable standardized evaluation. Beyond static prediction, UrbanWell defines temporal reasoning tasks, including future value forecasting from historical observations and temporal trend classification. We benchmark 15 state-of-the-art representative MLLMs in a zero-shot setting, providing a comprehensive comparative evaluation across spatial and temporal dimensions. Experimental results indicate that while MLLMs capture salient spatial and perceptual cues, their performance varies substantially across heterogeneous urban indicators spanning environment and subjective perception. UrbanWell serves as a unified benchmark for evaluating multimodal spatial and temporal reasoning in urban wellbeing analytics, offering a standardized testbed for systematic assessment and future research on multimodal urban intelligence. Our codes and datasets are accessible via https://github.com/axin1301/UrbanWell-Benchmark.
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