Abstract
Understanding the visual discrepancy and heterogeneity of different places is of great interest to architectural design, urban design and tourism planning. However, previous studies have been limited by the lack of adequate data and efficient methods to quantify the visual aspects of a place. This work proposes a data-driven framework to explore the place-informative scenes and objects by employing deep convolutional neural network to learn and measure the visual knowledge of place appearance automatically from a massive dataset of photos and imagery. Based on the proposed framework, we compare the visual similarity and visual distinctiveness of 18 cities worldwide using millions of geo-tagged photos obtained from social media. As a result, we identify the visual cues of each city that distinguish that city from others: other than landmarks, a large number of historical architecture, religious sites, unique urban scenes, along with some unusual natural landscapes have been identified as the most place-informative elements. In terms of the city-informative objects, taking vehicles as an example, we find that the taxis, police cars and ambulances are the most place-informative objects. The results of this work are inspiring for various fields—providing insights on what large-scale geo-tagged data can achieve in understanding place formalization and urban design.
Highlights
The physical setting of a place, such as a cityscape, is shaped heterogeneously by the difference in the development of culture, geography and history, as well as the interactions of dwellers over hundreds years of human settlement [1,2]
Formalizing the visual features, salient scenes and unique styles of places is crucial to urban design and urban management
This study proposes an image data-driven framework to learn visual knowledge of places and explore place-informative scenes and objects comprehensively, which can be applied to places of different types and scales, such as inter-city level, neighbourhood level and indoor level
Summary
The physical setting of a place, such as a cityscape, is shaped heterogeneously by the difference in the development of culture, geography and history, as well as the interactions of dwellers over hundreds years of human settlement [1,2]. The visual appearance of a place carries distinct information that differentiates it from other places. Understanding the visual differences between places is of great significance to urban studies, urban design and tourism planning. Because the visual cues are always subtle [4], people’s high-level knowledge of cultural difference and scene perception are usually required to perceive and distinguish places [6,7]
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