While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
Rapid urbanization and land-use changes are placing immense pressure on resources,infrastructure,and envi-ronmental sustainability.To address these,accurate urban simulation models are essential for sustainable develo...Rapid urbanization and land-use changes are placing immense pressure on resources,infrastructure,and envi-ronmental sustainability.To address these,accurate urban simulation models are essential for sustainable development and governance.Among them,Cellular Automata(CA)models have become key tools for pre-dicting urban expansion,optimizing land-use planning,and supporting data-driven decision-making.This review provides a comprehensive examination of the development of urban cellular automata(UCA)models,presenting a new framework to enhance individual UCA sub-modules within the context of emerging technologies,sus-tainable environments,and public governance.By addressing gaps in prior UCA modelling reviews-particularly in the integration and optimization of UCA sub-module technologies-this framework is designed to simplify UCA model understanding and development.We systematically review pioneering case studies,deconstruct current UCA operational processes,and explore modern technologies,such as big data and artificial intelligence,to optimize these sub-modules further.We discuss current limitations within UCA models and propose future pathways,emphasizing the necessity of comprehensive analyses for effective UCA simulations.Proposed solu-tions include strengthening our understanding of urban growth mechanisms,examining spatial positioning and temporal evolution dynamics,and enhancing urban geographic simulations with deep learning techniques to support sustainable transitions in public governance.These improvements offer data-driven decision support for environmental management,advancing policies that foster sustainable urban development.展开更多
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
文摘Rapid urbanization and land-use changes are placing immense pressure on resources,infrastructure,and envi-ronmental sustainability.To address these,accurate urban simulation models are essential for sustainable development and governance.Among them,Cellular Automata(CA)models have become key tools for pre-dicting urban expansion,optimizing land-use planning,and supporting data-driven decision-making.This review provides a comprehensive examination of the development of urban cellular automata(UCA)models,presenting a new framework to enhance individual UCA sub-modules within the context of emerging technologies,sus-tainable environments,and public governance.By addressing gaps in prior UCA modelling reviews-particularly in the integration and optimization of UCA sub-module technologies-this framework is designed to simplify UCA model understanding and development.We systematically review pioneering case studies,deconstruct current UCA operational processes,and explore modern technologies,such as big data and artificial intelligence,to optimize these sub-modules further.We discuss current limitations within UCA models and propose future pathways,emphasizing the necessity of comprehensive analyses for effective UCA simulations.Proposed solu-tions include strengthening our understanding of urban growth mechanisms,examining spatial positioning and temporal evolution dynamics,and enhancing urban geographic simulations with deep learning techniques to support sustainable transitions in public governance.These improvements offer data-driven decision support for environmental management,advancing policies that foster sustainable urban development.
