Urban land optimization in urban agglomerations plays an important role in promoting territorial spatial planning to achieve high-quality development, land ecological suitability (LES) is one of the important variables influencing its urbanization and needs to be considered in urban growth simulation and modeling. This research proposed a multi-objective urban land optimization (MULO) model based on the non-dominated sorting genetic algorithm II (NSGA-II) which integrates the LES assessment.

In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies.

The urban agglomeration at the Yangtze River Delta is one of the six most developed and populated urban agglomerations in the world. In recent years, with accelerating urbanization, the land use has changed significantly. Excessive construction aggravates ecological fragility. In this context, this paper first investigates the evolutionary processes and layout of the ecological space in the Yangtze River Delta. The root causes of various problems are then analyzed. Finally, suggestions for further improvement in both detailed tasks and governance aspects are proposed.