A Review: How Deep Learning Technology Impacts the Evaluation of Traditional Village Landscapes

Recently, the deep learning technology has been adopted in the study of traditional village landscape. More precisely, it's usually used to explore the representation of cultural heritage and the diversity of heritage information. In this study, we comprehensively reviewed these deep learning-related literatures for the evaluation of traditional village landscapes. E.g., the landscape image recognition led by the pixel-level semantic segmentation algorithm and image feature extraction technology enable user-centred exploration and make cultural heritage digitally and visually accessible. By suggesting a analytic framework using the pixel-level semantic segmentation algorithm and extracting image features, we attempted to identify the physical attributes and spatial characteristics of traditional village landscapes and further simulate the value perception thinking of experts and the public. Besides, we analysed the impact factors and correlation mechanism of spatial attributes to provide a scientific basis and technical support for the protection and utilization of traditional villages.

Automated summary

Recently, the utilization of deep learning technology in the study of traditional village landscape has gained attention. It is primarily used to explore the representation and diversity of cultural heritage information. The study comprehensively reviewed deep learning-related literature, specifically focusing on landscape image recognition using pixel-level semantic segmentation algorithm and image feature extraction technology. The objective was to make cultural heritage digitally and visually accessible for user-centred exploration. Additionally, the study attempted to identify the physical attributes and spatial characteristics of traditional village landscapes and simulate the value perception of experts and the public, offering a scientific basis and technical support for the protection and utilization of traditional villages.