Deep Learning-Based Heritage Building Assessment with Spatial Context

Abstract

Rapid urbanization threatens architectural heritage in developing regions, where limited conservation experts cannot assess thousands of potentially valuable buildings before irreversible modifications occur. This paper presents an automated screening system for heritage building identification using deep learning and spatial analysis. The proposed framework employs a dual-stream architecture combining YOLOv8 object detectionwith SegFormer semantic segmentation to extract architectural features from building facade photographs. These visual features are integrated with Geographic Information System (GIS) data to incorporate spatial context, recognizing that heritage buildings often cluster in historically significant areas. A hybrid weighting mechanism balances data-driven feature importance ($80 \%$) with expert architectural knowledge ($20 \%$) to ensure cultural sensitivity. Experimental evaluation on 1,500 buildings in Roi Et Province, northeastern Thailand, demonstrates the system’s effectiveness, achieving $87.6 \%$ classification accuracy while processing each building in approximately one second. In corporating spatial context improved performance by $6.4 \%$ over visual features alone. The transformer-based architecture proved particularly effective at identifying characteristic features such as paired windows and traditional wall patterns that distinguish heritage structures. This work provides a practical tool for large scale preliminary heritage assessment, enabling conservation authorities to efficiently allocate limited expert resources to high priority buildings while maintaining classification reliability suitable for initial screening purposes.