陈逸敏, 黎夏. 机器学习在城市空间演化模拟中的应用与新趋势[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1884-1889. DOI: 10.13203/j.whugis20200423
引用本文: 陈逸敏, 黎夏. 机器学习在城市空间演化模拟中的应用与新趋势[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1884-1889. DOI: 10.13203/j.whugis20200423
CHEN Yimin, LI Xia. Applications and New Trends of Machine Learning in Urban Simulation Research[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1884-1889. DOI: 10.13203/j.whugis20200423
Citation: CHEN Yimin, LI Xia. Applications and New Trends of Machine Learning in Urban Simulation Research[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1884-1889. DOI: 10.13203/j.whugis20200423

机器学习在城市空间演化模拟中的应用与新趋势

Applications and New Trends of Machine Learning in Urban Simulation Research

  • 摘要: 城市模拟自20世纪七八十年代兴起后,已成为城市研究的一种新范式,体现了计算思维对于城市研究的深刻影响。城市模拟方法建立在元胞自动机(cellular automata,CA)和机器学习基础上,形成了具有模拟城市复杂演化过程、实现多情景分析能力的城市CA模型。回顾了城市模拟的起源和发展,在归纳城市CA一般结构的基础上,讨论了机器学习方法在支持城市模拟方面的必要性和可行性,并进一步综述了机器学习与CA在城市研究中的新趋势,阐述了当前面临的主要挑战。

     

    Abstract: Urban simulation research originated between the 1980s and 1990s. Today urban simulation has become a new paradigm of urban research, which is an important outcome of computational thinking in urban research. Urban simulation methods are usually based on cellular automata (CA) and machine learning. A series of urban CA models have been developed to simulate complex urban evolution processes and associated multi-scenario analysis. This paper reviews the origin and progress of urban simulation research. With the discussion of urban CA's general structure, we explains the necessity and feasibility of machine learning methods to support urban simulation. Furthermore, we reviews the integration of machine learning and CA in urban research, and also discusses its new trends and emerging challenges.

     

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