Cross-Domain Architectural Component Detection and Cultural Adaptation Analysis in Migrant Architecture: A Case Study of Chinese Diaspora Buildings in Myanmar

Migrant architecture reflects selective continuity and reconfiguration across social contexts, but visual form alone cannot establish cultural origin or adaptation. This study develops an auditable human–AI framework for cross-domain architectural-component analysis and demonstrates it on Chinese diaspora buildings in Myanmar. A source corpus of 410 Minnan images with 5110 oriented bounding boxes supported source-only model development, while the target corpus contained 233 photographs from nine Myanmar sites. On a frozen 35-image, 271-component unseen-photo holdout from seven overlapping sites, YOLO11m-P2+C2PSA achieved 45.1% precision, 32.1% recall and 37.5% F1, whereas YOLO26m-P2+C2PSA achieved 56.3%, 18.1% and 27.4%, respectively. A prediction-blind second-reader check on seven selected images yielded 92.8% agreement F1 and median matched IoU of 0.774. Human verification and image-plane analysis identified selective continuity and contrasting component configurations; documentary evidence supported social–institutional embedding more strongly than Myanmar-derived material–morphological adaptation. The framework provides a traceable evidence chain in which detector outputs guide inspection without replacing human verification or independent historical evidence. The Myanmar application is an exploratory case study rather than a population-level estimate.