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Linguistic Landscapes on Street-Level Images

Department of Geography, Kyung Hee University, Seoul 02447, Korea
ISPRS Int. J. Geo-Inf. 2020, 9(1), 57;
Received: 25 December 2019 / Revised: 9 January 2020 / Accepted: 19 January 2020 / Published: 20 January 2020
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
Linguistic landscape research focuses on relationships between written languages in public spaces and the sociodemographic structure of a city. While a great deal of work has been done on the evaluation of linguistic landscapes in different cities, most of the studies are based on ad-hoc interpretation of data collected from fieldwork. The purpose of this paper is to develop a new methodological framework that combines computer vision and machine learning techniques for assessing the diversity of languages from street-level images. As demonstrated with an analysis of a small Chinese community in Seoul, South Korea, the proposed approach can reveal the spatiotemporal pattern of linguistic variations effectively and provide insights into the demographic composition as well as social changes in the neighborhood. Although the method presented in this work is at a conceptual stage, it has the potential to open new opportunities to conduct linguistic landscape research at a large scale and in a reproducible manner. It is also capable of yielding a more objective description of a linguistic landscape than arbitrary classification and interpretation of on-site observations. The proposed approach can be a new direction for the study of linguistic landscapes that builds upon urban analytics methodology, and it will help both geographers and sociolinguists explore and understand our society. View Full-Text
Keywords: linguistic landscape; street-level images; computer vision; urban analytics; urban computing linguistic landscape; street-level images; computer vision; urban analytics; urban computing
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MDPI and ACS Style

Hong, S.-Y. Linguistic Landscapes on Street-Level Images. ISPRS Int. J. Geo-Inf. 2020, 9, 57.

AMA Style

Hong S-Y. Linguistic Landscapes on Street-Level Images. ISPRS International Journal of Geo-Information. 2020; 9(1):57.

Chicago/Turabian Style

Hong, Seong-Yun. 2020. "Linguistic Landscapes on Street-Level Images" ISPRS International Journal of Geo-Information 9, no. 1: 57.

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