The analysis of intelligent urban form generation design based on deep learning

In response to the growing demand for intelligent solutions in urban planning, this study constructs a deep learning-based framework for generating intelligent urban morphology, effectively addressing pressing real-world challenges. At the outset, the study explores the core concepts of green and ecological principles within the evolution of contemporary urban forms, establishing a robust theoretical foundation for subsequent investigations. The study provides a detailed explanation of the practical application paradigms of deep learning, encompassing meticulously selected technical methodologies, carefully designed algorithmic structures, and an optimized parameter configuration system. Together, these elements form a comprehensive technological application framework. An innovative application of convolutional neural networks is introduced for the in-depth analysis and processing of urban street imagery. This advancement enables critical urban planning functions, including road network design, detailed analysis of building distributions, optimization of public facility layouts, and dynamic traffic flow analysis. These capabilities address the key limitations of traditional planning methods by enhancing intelligent analysis and precise decision-making. To evaluate the model''s performance quantitatively, a systematic testing scheme is developed and implemented, covering various scenarios, including daytime and nighttime conditions. This approach ensures a comprehensive assessment of the precision and effectiveness of each functionality. The core significance and contributions of this study are encapsulated in its empirical findings. The proposed model achieves accuracy and fit metrics exceeding 93% across all testing dimensions, representing a significant advancement that provides robust and targeted support for urban planning practices. By integrating deep learning technologies into the intelligent urban morphology generation framework, the study successfully implements critical functions such as efficient road network planning and scientific analysis of building distributions. Furthermore, the study introduces cutting-edge technological tools and innovative methodologies to the urban planning discipline, advancing the development of intelligent urban planning. Its contributions are of profound value in both theoretical innovation and practical application, offering transformative potential for the field.