Closed-Set vs. Open-Vocabulary Object Detectors for Urban Architectural Typology Classification: A Comparative Study on Athenian Heritage Buildings

Abstract

Architectural typology classification plays an important role in large-scale documentation and analysis of urban cultural heritage. Recent advances in computer vision enable automated approaches for detecting and categorizing buildings from street-level imagery, yet the suitability of different detection paradigms for architectural typology analysis remains insufficiently explored. Despite recent advances in computer vision for architectural analysis, no systematic comparative study has evaluated closed-set CNN-based detectors against open-vocabulary vision–language grounding models for urban architectural typology classification. This study presents a comparative evaluation of closed-set convolutional object detectors and open-vocabulary vision–language grounding models for the classification of Athenian architectural typologies. A dataset of 3349 street-view images containing 11,111 annotated building instances was compiled and organized into five typological categories: Neoclassical, Neoclassical-Eclectic, Interwar-Eclectic, Interwar, and Postwar. The experiments compare several YOLO-based detection configurations with Grounding DINO under zero-shot inference, parameter-efficient adaptation (e.g., Kiw Rank Adaptation—LoRA), and full fine-tuning. Results show that supervised YOLO-based models achieve robust detection and classification performance with high localization accuracy and consistent typology discrimination in dense urban scenes. In contrast, open-vocabulary grounding models demonstrate limited reliability in zero-shot settings and require substantial adaptation to approach comparable performance levels. Analysis of confusion patterns further reveals that most classification errors originate from intrinsic architectural similarities between transitional styles rather than from model instability. The findings highlight the advantages of supervised object detection frameworks for scalable urban heritage documentation and provide insights into the current limitations of vision–language models for fine-grained architectural typology classification.