Rattanapitak, Wirat
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Rattanapitak, Wirat
Main Affiliation
Email
wirat.ra@kmitl.ac.th
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Item type:Publication, Deep Learning-Based Heritage Building Assessment with Spatial Context(2025-01-01); ; ; Sirikitsathian, PhatthananRapid 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, GIS and Street View Integration: Analyzing Spatial Factors of Motorcycle Taxi Stands in Bangkok(2024-01-01); Sirikitsathian, PhatthananMotorcycle taxis play a vital role in addressing lastmile connectivity challenges in Bangkok's complex urban transportation system. However, the factors influencing the spatial distribution of these informal transport services remain poorly understood. Kernel Density Estimation (KDE) was employed to examine the spatial distribution patterns, revealing two primary high-density clusters of stands in southern Chatuchak. Proximity analysis quantified relationships between the 208 stand locations and urban infrastructure factors, finding strategic positioning near mass transit stations (avg. 1.79 km), on sidewalks (70.5%), and in mixed-use areas with predominantly residential (33.19%) and commercial (29.32%) land use within a 100m radius. A decision tree model identified residential area proportion (importance 0.3423) and distance from metro stations (0.2935) as key predictors of stand presence, but achieved moderate accuracy (52.38%). The results highlight the close integration of motorcycle taxi stands within the urban fabric and their role in enhancing accessibility.