Mapping linguistic landscapes and displacement: Urban-scale analysis of internally displaced people in Kyiv using street view imagery and computer vision

Internally displaced people (IDPs) who fled conflict zones in Donetsk and Luhansk beginning in 2013 relocated to other cities in Ukraine. Their Russian-language preference can be quantitatively and qualitatively observed and measured in the urban textual environment, or linguistic landscape (LL) of Kyiv, the bilingual Ukrainian capital. This study examines the LL at a city-wide scale, leveraging Google Street View imagery with deep learning text detection, recognition, and script identification, alongside geospatial tools for large-scale analysis. The approach enables systematic, reproducible mapping of machine-detectable public-facing signage along road networks, supporting analysis of spatial and temporal associations between population relocation and LL change. Analyses at the administrative and road network levels reveal differing language preferences between primary and residential roads. Results indicate clustering of the Russian keyword ‘аренда’ (rent) near IDP relocation sites during the relocation period and an observable increase in Ukrainian-language preference after the implementation of the 2019 language policy.