Visual Language Translation Analysis and Scenic Beauty Estimation of Mountain Stream Facilities

• Published in: Water (Basel) Vol. 14; no. 22; p. 3605
• Main Authors: Peng, Szu-Hsien, Huang, Chun-Yu, Chen, Su-Chin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Aesthetics; Architecture; Beauty; Computational linguistics; Construction; Dam removal; Dams; Data analysis; Design; Language; Language processing; Natural language interfaces; Natural language processing; Perceptions; Preferences; Qualitative research; Questionnaires; Research methodology; Social networks; Social sciences; Translating and interpreting; Taiwan; Japan
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14223605

The aesthetic design of mountain stream facilities has not received much attention in Taiwan. In recent years, in addition to safety concerns, the focus has increasingly been on landscape assessment or the sense of integration with the environment. This research is the first attempt to use visual language translation in qualitative research analysis for the landscape assessment of mountain stream facilities. This method is different from a traditional qualitative narrative analysis. It also addresses the shortcomings of previous quantitative analysis methods, in which the topic discussions are too limited. First, mountain stream engineering projects are selected as the research objects. This study uses questionnaire analysis and on-site surveys to summarize the elements, representations, and perceptions of the mountain stream facilities of the subjects concerned to examine their preferences for the visual system. Furthermore, we also employ the scenic beauty estimation (SBE) method for a comparison between the qualitative and quantitative analyses. This study proposes a new method using visual language translation and SBE that combines the features of qualitative research and quantitative analysis. However, the potential limitations include an inability to have a large sample number and the biases caused by the cultural, regional, or personal characteristics of the subjects.