Plane graph generation is a core part of generative design research,yet the lack of datasets constrains the development of generative design research.In order to solve the data bottleneck problem cost-effectively,this study proposes and validates an algorithm-driven data enhancement framework based on algorithms.Firstly,combining deep learning and generative design task characteristics,three key steps of outer environment cutting,hierarchical training,and curve optimization are proposed,based on which a complete and effective data enhancement framework for landscape plan drawings is constructed.Secondly,ablation experiments are conducted on each part of the framework based on the test set to verify the effectiveness of the framework.Finally,the framework is applied to generate a set of high-quality datasets,and the data are applied to the three main mainstream tasks of"image segmentation","layout generation"and"planar rendering"in the design of landscape garden generation to verify the framework in practice.
维普
万方数据