Global variations of urban form: Characterization and quantification through intelligent remote sensing image analysis
• A globally applicable modular urban form modeling framework is proposed to provide a refined and comprehensive perspective for characterizing urban form variation. • The Modular Urban Local Form (MULF) comprises 21 distinct categories—three natural types and eighteen artificial types—that are identified through systematic analysis. • The Modular Urban Form Prototype (MUFP) offers a valuable approach for understanding the variation of urban spatial configurations across the globe. Urban form serves as a crucial lens for visually discerning the complexities and evolving patterns of cities worldwide. Despite its importance, existing research lacks the capacity to effectively characterize global variations in urban form. To address this limitation, our study proposes a novel urban form typology—Modular Urban Local Form (MULF)—comprising three natural categories and eighteen artificial categories, aimed at modeling urban physical space. This typology is grounded in the identification of Modular Urban Form Prototype (MUFP) across global cities. The global variation analysis of MULF reveals two distinct patterns. As urban population size increases, the proportion of local form categories characterized by large buildings decreases from 0.50 to 0.24. As urban income levels rise, the proportion of compact building forms declines from 0.92 to 0.26. The MULF global mapping offers a valuable perspective for understanding the form variation and evolution of cities with different population sizes and income levels. The insights derived from the MULF system highlight its potential as a feasible and globally applicable framework for interdisciplinary research on urban sustainable development.
Authors
- Qihao Weng (ORCID: https://orcid.org/0000-0002-2498-0934)
- Geng Sun (ORCID: https://orcid.org/0000-0001-6713-5151)
- Cheolhee Yoo (ORCID: https://orcid.org/0000-0002-3922-2300)
- Linlin Lu (ORCID: https://orcid.org/0000-0003-1647-1950)
- Yongze Song (ORCID: https://orcid.org/0000-0003-3420-9622)
- Yinxia Cao (ORCID: https://orcid.org/0000-0002-5896-2814)
- Jie Chen (ORCID: https://orcid.org/0000-0003-2449-9793)
- Ya Guo
- Chunhong Zhao
Publication Details
- Journal
- Sustainable Cities and Society
- Published
- 2025-05-03
- DOI
- https://doi.org/10.1016/j.scs.2025.106424
- Citations
- 2
- Primary Topic
- Remote Sensing and Land Use
- Type
- article
- Field-Weighted Citation Impact
- 2.60