An Integrated Model Based on CNN-Transformer and PLUS for Urban Expansion Simulation in the Yangtze River Delta, China.
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| Title: | An Integrated Model Based on CNN-Transformer and PLUS for Urban Expansion Simulation in the Yangtze River Delta, China. |
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| Authors: | Ma, Linyu1 (AUTHOR), Xiao, Jue1 (AUTHOR), Teng, Gan1 (AUTHOR), Zhang, Ting1 (AUTHOR), Chen, Longqian1 (AUTHOR) chenlq@cumt.edu.cn |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p1071. 21p. |
| Subjects: | Transformer models, Zoning, Urban growth, Machine learning, Spatial variation |
| Geographic Terms: | Yangtze River Delta (China), China |
| Abstract: | Highlights: What are the main findings? A GCTP model is proposed by coupling guided zoning, CNN-Transformer and PLUS. The GCTP model achieves high accuracy in urban expansion simulation. What are the implications of the main findings? Guided zoning effectively mitigates spatial non-stationarity in large regions. CNN-Transformer captures long-range spatial dependencies to optimize land patterns. Land use changes within urban agglomerations exhibit significant spatiotemporal heterogeneity and regional diversity. In urban agglomeration land simulation, traditional models often struggle to systematically capture these variations. We introduce the GCTP, a novel framework that integrates guided Geographical zoning, Convolutional Neural Networks (CNN)-Transformer, and the Patch-generating Land Use Simulation (PLUS) model. Initially, guided K-means clustering was employed for geographic zoning to characterize regional spatial non-stationarity. Then, a CNN-Transformer network leveraged self-attention mechanisms to capture multi-scale spatial correlations, obtaining pixel-level development probabilities. Finally, these probabilities were fused with PLUS- Land Expansion Analysis Strategy (LEAS) outputs to drive PLUS- Cellular Automata with multi-type Random Seeds (CARS) for patch-level simulation. The results demonstrate the following: (1) The embedding of guided zoning enabled the model to achieve an Overall Accuracy (OA) of 0.941, effectively mitigating global simulation bias. (2) The optimal simulation performance occurred at a fusion weight of 0.81, yielding a Kappa of 0.8917 and an Figure of Merit (FoM) of 0.3830, significantly exceeding a single model. (3) The 2030 simulation indicates that the GCTP model effectively reduces isolated pixels at urban fringes. The GCTP generates neighborhood patterns with high spatial compactness and geographic consistency. This study highlights the significant advantages of integrating long-range spatial perception with geographical heterogeneity constraints in the land expansion simulation of urban agglomerations. The findings support more precise territorial spatial planning practices. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A GCTP model is proposed by coupling guided zoning, CNN-Transformer and PLUS. The GCTP model achieves high accuracy in urban expansion simulation. What are the implications of the main findings? Guided zoning effectively mitigates spatial non-stationarity in large regions. CNN-Transformer captures long-range spatial dependencies to optimize land patterns. Land use changes within urban agglomerations exhibit significant spatiotemporal heterogeneity and regional diversity. In urban agglomeration land simulation, traditional models often struggle to systematically capture these variations. We introduce the GCTP, a novel framework that integrates guided Geographical zoning, Convolutional Neural Networks (CNN)-Transformer, and the Patch-generating Land Use Simulation (PLUS) model. Initially, guided K-means clustering was employed for geographic zoning to characterize regional spatial non-stationarity. Then, a CNN-Transformer network leveraged self-attention mechanisms to capture multi-scale spatial correlations, obtaining pixel-level development probabilities. Finally, these probabilities were fused with PLUS- Land Expansion Analysis Strategy (LEAS) outputs to drive PLUS- Cellular Automata with multi-type Random Seeds (CARS) for patch-level simulation. The results demonstrate the following: (1) The embedding of guided zoning enabled the model to achieve an Overall Accuracy (OA) of 0.941, effectively mitigating global simulation bias. (2) The optimal simulation performance occurred at a fusion weight of 0.81, yielding a Kappa of 0.8917 and an Figure of Merit (FoM) of 0.3830, significantly exceeding a single model. (3) The 2030 simulation indicates that the GCTP model effectively reduces isolated pixels at urban fringes. The GCTP generates neighborhood patterns with high spatial compactness and geographic consistency. This study highlights the significant advantages of integrating long-range spatial perception with geographical heterogeneity constraints in the land expansion simulation of urban agglomerations. The findings support more precise territorial spatial planning practices. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18071071 |
