| 城市扩展元胞自动机多结构卷积神经网络模型 |
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| 引用本文： | 谢志文,王海军,张彬,黄鑫鑫.城市扩展元胞自动机多结构卷积神经网络模型[J].测绘学报,2020,49(3):375-385. |
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| 作者姓名： | 谢志文 王海军 张彬 黄鑫鑫 |
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| 作者单位： | 1. 武汉大学资源与环境科学学院, 湖北 武汉 430079;2. 武汉大学地理信息系统教育部重点实验室, 湖北 武汉 430079 |
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| 基金项目： | 国家自然科学基金（41571384） |
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| 摘 要： | 传统的城市扩展元胞自动机(CA)模型是基于单个元胞的变量信息挖掘来构建转换规则的。针对这一问题,本文基于多结构卷积神经网络提出从区域特征出发且顾及区域多尺度特征挖掘转换规则的城市扩展元胞自动机模型(MSCNN-CA),并以武汉主城区和上海浦东新区为例,模拟了两个试验区2005—2015年期间城市扩展过程。模型验证表明:与逻辑回归和神经网络相比,本文构建的3个单一结构的卷积神经网络元胞自动机(CNN-CA)模型在4个指标(Kappa系数、FoM(figure of merit)值、命中率(h)和错误率(m))上都有不同程度的提高。特别是FoM指数,在武汉主城区提高了23.3%~29.4%,在上海浦东新区提高了20.3%~28.5%。此外,MSCNN-CA模型与3个单一结构的CNN-CA模型相比,在各个指标上也有所改善,FoM指数在武汉主城区提高了0.8%~4.8%,上海浦东新区提高了2.8%~7.8%。两个试验区的模拟结果表明:相比传统CA模型,基于多结构卷积神经网络的城市扩展元胞自动机模型(MSCNN-CA)能够有效提高城市扩展模拟的精度,更真实地反映城市扩展空间演变过程。相比单结构的卷积神经网络CA模型,多结构卷积神经网络CA模型的稳定性和模拟结果准确性有所提升。
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| 关 键 词： | 城市扩展 元胞自动机 多结构卷积神经网络 地理模拟 |
| 收稿时间： | 2019-04-22 |
| 修稿时间： | 2019-10-21 |
Urban expansion cellular automata model based on multi-structures convolutional neural networks |
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| XIE Zhiwen,WANG Haijun,ZHANG Bin,HUANG Xinxin.Urban expansion cellular automata model based on multi-structures convolutional neural networks[J].Acta Geodaetica et Cartographica Sinica,2020,49(3):375-385. |
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| Authors: | XIE Zhiwen WANG Haijun ZHANG Bin HUANG Xinxin |
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| Institution: | 1. School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China;2. Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China |
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| Abstract: | Based on multi-structures convolutional neural networks, this paper proposes an urban expansion cellular automata model (MSCNN-CA) considering the multi-scale neighborhood information to explore the problem of traditional cellular automata (CA) models merely accounting for a single pixel's factors while mining the urban development suitability. This paper took the main urban zone of Wuhan and Pudong New District of Shanghai as examples to simulate the urban expansion process of the two study areas from 2005 to 2015. The experimental results show that, compared with two traditional CA models (LR, ANN), the three single-structure CNN-CA models constructed in this paper have different degrees of improvement in Kappa coefficient, FoM coefficient, hit rate (h) and miss rate (m). In particular, the FoM coefficient is increased by 23.3%~29.4% in the main urban zone of Wuhan and 20.3%~28.5% in Pudong New District of Shanghai. In addition, compared with the three single-structure CNN-CA models, the MSCNN-CA model is also improved in various indicators. Such as, the FoM coefficient is increased by 0.8%~4.8% in the main urban zone of Wuhan and 2.8%~7.8% in Pudong New District of Shanghai. The two study areas' simulation results show that, compared with the traditional CA models, the urban expansion cellular automata model based on multi-structures convolutional neural network (MSCNN-CA) can effectively improve the accuracy of urban expansion simulation and more realistically reflect the evolution process of urban expansion. Besides, both the stability and the accuracy of the MSCNN-CA model are improved comparing with the single-structure convolutional neural network CA model. |
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| Keywords: | urban expansion cellular automata multi-structures convolutional neural networks geographic simulation |
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