Urbanization trend prediction and spatial resource optimization using optical remote sensing and machine learning

The accelerating pace of urbanization has intensified demands for accurate trend forecasting and efficient territorial space resource allocation. As a core component of smart city development, data-driven spatial modeling plays a crucial role in supporting scientific decision-making and intelligent governance. Optical remote sensing provides rich multi-temporal and spatial information for monitoring land use dynamics, while machine learning offers powerful tools for modeling complex urban growth patterns. This study proposes a predictive modeling and optimization framework that integrates optical remote sensing imagery with machine learning algorithms to analyze new urbanization trajectories and optimize land resource deployment. The approach incorporates land cover classification, urban expansion prediction, and spatial optimization modules, leveraging deep learning models and adaptive classification techniques. Empirical validation is conducted using multisource urban datasets across rapidly developing regions, with results showing improved accuracy in land use forecasting and enhanced spatial planning support compared to conventional methods. The proposed framework demonstrates strong potential for scalable, data-driven decision-making in sustainable urban development, and provides a technical foundation for intelligent territorial governance under complex environmental conditions.