Quantifying the Characteristics of the Local Urban Environment through Geotagged Flickr Photographs and Image Recognition

Abstract

Urban environments play a crucial role in the design, planning, and management of cities. Recently, as the urban population expands, the ways in which humans interact with their surroundings has evolved, presenting a dynamic distribution in space and time locally and frequently. Therefore, how to better understand the local urban environment and differentiate varying preferences for urban areas has been a big challenge for policymakers. This study leverages geotagged Flickr photographs to quantify characteristics of varying urban areas and exploit the dynamics of areas where more people assemble. An advanced image recognition model is used to extract features from large numbers of images in Inner London within the period 2013–2015. After the integration of characteristics, a series of visualisation techniques are utilised to explore the characteristic differences and their dynamics. We find that urban areas with higher population densities cover more iconic landmarks and leisure zones, while others are more related to daily life scenes. The dynamic results demonstrate that season determines human preferences for travel modes and activity modes. Our study expands the previous literature on the integration of image recognition method and urban perception analytics and provides new insights for stakeholders, who can use these findings as vital evidence for decision making.

Crowd-Sourced City Images: Decoding Multidimensional Interaction between Imagery Elements with Volunteered Photos

“…Recently, computer vision models have been used to successfully localize multiple urban objects or classify urban scenes based on a group of themes [40][41][42]. For example, scene recognition techniques can reveal the specific type of shooting place to reveal the spatial distribution of different imagery elements [3,[43][44][45] and investigate the characteristics, similarities and differences among cities [3,14,43]; discriminant clustering and image object detection are used to extract and map visual elements of local characteristics from photos [46,47]. The applications of computer vision techniques with volunteered, geotagged photos in social media are providing new perspectives about people's images of specific places or areas with the advantages of wide coverage, instant updates, vector-based resolution, and an individual focus.…”

Exploring Spatiotemporal Change of City and Village from Remote Sensing Using Multi-Branch Networks

“…Kim, Dongeun and Kang, Youngok et.al [30] proposed a understanding tourists' urban images with geotagged photos using convolutional neural networks.With the continuous increase of the urban population, the human gathering area has gradually evolved into a local dense temporal and spatial dynamic distribution. In order to better understand the urban environment, Chen, Meixu and Arribas-Bel et al [31] constructed an advanced image recognition model and used The marked Flickr pictures are used to train the neural network to quantify the feature information of different cities. Jayasuriya, Maleen and Arukgoda, Janindu et.al [32] present a novelty localising PMDs perception methods of urban street via convolutional neural networks.…”

Culturally Diverse Street-Level Urban Activities through the Lens of Digital Footprints

“…These online social interactions leave digital footprints (spatiotemporal user-generated opinions, check-ins, photographs), which, once interpreted, are highly valuable for urban research purposes [21] and for informing decisionmaking processes. It has now been over a decade since the footprints generated by users of virtual social media platforms have proved to be useful for detecting key physical and behavioural aspects of the urban environment [22][23][24] and discerning phenomena that are hard to appreciate directly by the human senses: people s perceptual responses to the environment [25,26], the cultural diversity of an urban setting [27], and other complex non-physical phenomena, such as the sense of place [28] and the character and vibrancy of local urban life [29][30][31][32].…”