Urban Planning and Street Layout Optimization Based on Deep Learning

Accurately extracting urban land classifications and forest information is crucial in urban planning and construction, as well as significant for promoting sustainable urban development. With advanced remote sensing technology, acquiring ultra-high spatial resolution (VHSR) remote sensing images enables us to accurately capture urban surface information, providing powerful data support for urban planning. Deep learning (DL) has brought new solutions for urban land use classification and urban forest information extraction. Meanwhile, through big data analysis and DL algorithms, we can accurately predict the traffic flow of urban road networks, optimize public transportation route planning, and improve the efficiency and utilization of public transportation. This article proposes an innovative DL-based urban planning and street layout optimization model, which can accurately classify various land features in remote sensing images and optimize public transportation routes, accordingly, providing a scientific basis for urban planning. The experimental results indicate that the proposed model has significant advantages in improving urban planning efficiency and promoting sustainable development of urban transportation.