基于深度风格学习的城市景观的独特性与相似性分析
发布时间：2022-04-25
点击次数：
发表刊物：International Journal of Geo-Information
关键字：city landscape; social media images; Flickr; style distance; visual similarity
摘要：The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to focus on local urban characteristics while learning new knowledge. Therefore, it is particularly important to explore the characteristics of cities’ landscapes. Previous researchers have studied them from different perspectives through social media data such as element types and feature maps. They only considered the content information of a image. However, social media images themselves have a “photographic cultural” character, which affects the city character. Therefore, we introduce this characteristic and propose a deep style learning for the city landscape method that can learn the global landscape features of cities from massive social media images encoded as vectors called city style features (CSFs). We find that CSFs can describe two landscape features: (1) intercity landscape features, which can quantitatively assess the similarity of intercity landscapes (we find that cities in close geographical proximity tend to have greater visual similarity to each other), and (2) intracity landscape features, which contain the inherent style characteristics of cities, and more fine-grained internal-city style characteristics can be obtained through cluster analysis. We validate the effectiveness of the above method on over four million Flickr social media images. The method proposed in this paper also provides a feasible approach for urban style analysis.
合写作者：李博
论文类型：期刊论文
是否译文：否
发表时间：2021-10-29
收录刊物：SCI