Simulating mixed land-use change under multi-label concept by integrating a convolutional neural network and cellular automata: a case study of Huizhou, China

Abstract

Cellular automata (CA) model is a useful tool for simulating spatiotemporal changes of land-use evolution. However, previous CA models have usually ignored the inhomogeneous and mixed land-use situations and simplified the land grid cell as one single land-use type. This study developed a multi-label (ML) convolutional neural network CA (ML-CNN-CA) model to simulate the complex evolution of mixed land uses. The multi-label learning strategy in the proposed model can formulize mixed land uses by labeling multiple land-use types to each grid cell. The transition rules of CA were modified to accommodate the multi-label learning strategy and mined by coupling a CNN network and three interactive mechanisms, including the neighborhood effect, adaptive inertia coefficient, and random factor. The proposed ML-CNN-CA model was applied and examined in a fast-developing city (Huizhou) in the China’s Pearl River Delta region for the mixed land-use simulation during 2009–2013 and 2013–2020. The ML-CNN-CA model with different CNN architectures and a previous artificial neural network (ANN)-based multi-label CA model were also implemented for comparisons. Results show the capability of the ML-CNN-CA model to simulate fine-scale mixed land-use changes with satisfactory performances. Specifically, the simulated mixed land-use patterns agree well with the actual land uses from the morphological perspective. Furthermore, the quantitative assessments demonstrate the performance of the proposed model, showing an accuracy value of 0.912 for 2009–2013 and 0.896 for 2013–2020, and a hamming loss value of 0.048 for 2009–2013 and 0.055 for 2013–2020. The comparisons also show the best performance of the ML-CNN-CA model with the VGG-based architecture and significant outperformance of the proposed model against the previous ANN-based CA model. Multiple sensitivity analyses were also conducted to investigate the uncertainty of the proposed model. The ML-CNN-CA model proposed in this study can provide a new tool for better simulation of fine-scale mixed land-use changes and is expected to help formulate urban planning guidelines and achieve sustainable urban development.

Keywords

• land-use change
• multi-label learning
• mixed land use
• cellular automata
• convolutional neural network