@article{Zhang2026, 
author = {Yecheng Zhang and Ying Long},
title = {Towards building-scale urban analytics and simulation},
year = {2026},
journal = {Building Simulation},
volume = {19},
number = {5},
pages = {1171-1176},
keywords = {building studies, urban studies, urban simulation, applied urban model},
url = {https://www.sciopen.com/article/10.1007/s12273-026-1461-9},
doi = {10.1007/s12273-026-1461-9},
abstract = {Buildings serve as the basic cells of a city, forming a complex urban system that integrates physical and social characteristics. While urban analytics and simulation have traditionally been constrained by data availability and computational complexity, recent advancements in sensing, AI and high performance computing have made building-scale inquiry increasingly feasible. This perspective proposes and highlights Building-Scale Urban Analytics and Simulation through four key pillars. First, it calls for a unified modeling standard that aligns the semantic depth of building engineering with the geographic breadth of urban science. Second, it highlights the need for constructing comprehensive building datasets with full spatiotemporal coverage through AI-driven data foundations. Third, it advocates for the use of generative AI to diagnose building quality and efficacy. Fourth, it proposes a bottom-up simulation paradigm that integrates AI with generative rules to model complex morphological and behavioral transformations. This vision provides a critical bridge between building studies and urban studies.}
}