Incorporating sparse model machine learning in designing cultural heritage landscapes - Goodarzi, Parichehr; Ansari, Mojtaba; Pour Rahimian, Farzad; Mahdavinejad, Mohammadjavad; Park, Chansik (Automation in construction, 2023)
Managing, protecting, and the evolutionary development of historical landscapes require robust frameworks and processes for forming datasets and advanced decision support tools. Despite the great potential, using pattern language, machine learning, and regenerative and generative design tools has yet to be adopted in historic landscape research due to the need for suitable training datasets. To address this theoretical and technical gap, this paper describes a three-step workflow, namely photogrammetry, feature extraction and discriminative feature analytics, to help facilitate the use of advanced ML tools for cultural heritage decision support. Sparse-Learning-Modelling (SLM) was used to help with feature extraction from small datasets. The developed tool was successfully tested on the 3D point cloud models of 13 heritage sites, and these could be replicated in other heritage sites with distinctive Cultural DNA worldwide. The findings of this research can extend the discourse of adopting advanced AI/digital tools in heritage landscape design. •A theoretical framework is provided for scarce data-driven regenerative decision support tools.•Solutions support designers in meaningful place-making of the next generation landscapes based on historic landscape’ DNA.•Regenerative-adaptive pattern languages are supported for heritage landscapes and meaningful place-making in future.•A sparse learning model is used to address few-shot problems.•The tool distinguishes characteristics of different point clouds, including sceneries, climate, slope, and orientation.