Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning

Abstract

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.

Social Media Image and Computer Vision Method Application in Landscape Studies: A Systematic Literature Review

“…In the urban planning and management branch, researchers use crowdsourced images to develop an overall image of a city comprising various intentional elements or to focus on the public perception and use of certain places [42,43]. Distinguishing different urban styles through landscape feature quantification is another important research theme [44]. Further, in the tourism management branch, the visual content analysis of tourism photographs is an effective method for examining tourists' perceptions of destination images and tapping into tourists' activities [45].…”

“…[63,64] Transfer learning A machine learning method in which a pretrained model developed for a task was reused as the starting point for a model on a second task [27]. [44,65] Other Instead of relying on pretrained models, the authors propose new architectures to train models or use other computer vision algorithms to process images. [52,60] Figure 6 depicts the trends in researchers' selection of computer vision methods.…”

Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review

“…In urban design and landscape studies, the introduction of a deep learning image classification frameworks can answer the major research questions of resident/tourist behavior and perception, the identification of urban landscape features, landscape assessment, and natural disaster assessment. ResNet-50 [17,136], ResNet-101 [137], ResNet-152 [138,139], VGGNet [18,114,[140][141][142], Densenet-161 [143], and Inception v3 [144] are the deep learning frameworks used in these studies, with VGG-16 and ResNet being the most commonly used. Essentially, the deep learning approach straddles the limits of traditional approaches in terms of accuracy and data volume for the purpose of image content analysis.…”

Urban Spatial Image Acquisition and Examination Based on Geographic Big Data

“…During this urbanization process, urban spaces have undergone drastic expansion, leading to profound transformations in internal spatial configurations. Nonetheless, this development has also given rise to the "thousand cities, one face" issue, wherein cities across regions exhibit excessive uniformity and a lack of regional and local cultural distinctiveness, significantly diminishing their inter-city identifiability and appeal [2]. A people-oriented urban perspective necessitates a deeper and more scientific understanding of cities, placing emphasis on the cultivation of urban characteristics and spatial structures.…”