Mapping language at scale: combining street-view imagery, geospatial analysis, and machine learning in the study of Indonesian urban landscapes - Aulia Cika Kartika (CV Narasi Khatulistiwa Indonesia, 2026)
Background: Urban linguistic landscapes remain difficult to examine at metropolitan scale because conventional fieldwork offers interpretive depth but limited spatial coverage, while automated image analysis often overlooks linguistic and social context. Objective: This study aims to integrate street-view imagery, machine-assisted text recognition, geospatial analysis, and multimodal interpretation to map publicly visible languages across differentiated urban environments in Jakarta. Method: This study applies a spatially explicit multimethod design to ninety validated signs extracted from nine eligible public images, combining sign detection, OCR, language identification, human validation, corridor-based mapping, and social-semiotic coding. Results: Findings indicate that machine-readable visibility is uneven, with standardized regulatory and commercial signs more reliably detected than temporary, occluded, stylized, or mixed-script signs. Spatial patterns show Indonesian concentrated in regulatory corridors, English more visible in corporate and retail settings, and Chinese localized within Glodok’s commercial landscape. Implication: Institutional analysis further demonstrates that language hierarchy is produced through sign ownership, communicative function, placement, typography, colour, materiality, and logo positioning rather than frequency alone. Novelty: This study contributes a reproducible framework that connects computational accuracy, exposure-aware spatial comparison, and socially grounded interpretation while preventing linguistic visibility from being conflated with speaker population, language vitality, or causal urban influence within a transparent and human-validated urban GeoAI research architecture.