Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata
Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata
复制标题
通过将卷积神经网络与基于向量的元胞自动机集成来模拟城市土地利用变化
DOI：
10.1080/13658816.2020.1711915
复制
发表时间：
2020-01-16
影响因子：
5.7
通讯作者：
Zhou, Jianfeng
中科院分区：
文献类型：
--
作者：
Zhai, Yaqian;Yao, Yao;Zhou, Jianfeng
矢量元胞自动机（VCA）模型已在细尺度土地利用变化模拟中得到应用。然而，在探索细胞的过渡适合性时，很少考虑驱动因子的邻域效应，导致模拟精度较低。本研究提出一个卷积神经网络（CNN）-VCA模型，该模型采用CNN来提取不规则形状单元邻域内驱动因子的高级特征，并在邻域水平上发现多个土地利用变化与驱动因子之间的关系。应用该模型对深圳市城市土地利用变化进行了模拟。与其他机器学习方法的VCA模型相比，CNN-VCA模型获得了最高的仿真精度（优值= 0.361）。结果表明，CNN-VCA模型能够有效揭示多驱动因子对地块发展潜力的邻域效应，并能获得更多的地块形态特征细节。同时，模拟了生态控制策略下2020年和2025年的土地利用格局，为城市规划提供决策支持。
ABSTRACT Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.