Advancing Urban Environment Studies in Murcia, Spain through an Automated Façade Image Classification Model

Over the past few decades, the rapid growth of cities has evolved into a significant social, demographic, and architectural phenomenon, highlighting the vital importance of urban planning in fostering sustainable development. In this context, machine learning has emerged as a game-changing discipline, utilizing advanced algorithms to reshape traditional approaches to urban data management and analysis.This study combines Geographic Information Systems (GIS), Deep Learning techniques, and verified data from the General Directorate of the Spanish Cadastre to perform a comprehensive analysis of the urban environment through façade images in Murcia, one of Spain’s most dynamic metropolitan areas.Leveraging the clustering analysis of the studied variables, an automated binary classification model for façade images was developed using the pretrained EfficientNetB0 architecture in Python. To enhance interpretability, heat maps were generated to visualize the regions the model focuses on during classification. These heat maps reveal the critical features of the facades that guide the model’s decisions, providing valuable insights into the key factor influencing the classification process.The results were integrated into ArcGIS PRO, using the cadastral reference of the properties as a key attribute for a detailed spatial analysis. This approach revealed two significant areas linked to the metropolitan growth of Murcia, laying a strong foundation for future urban studies in the region.

Funding: Twin-ER: Earthquake Risk Pilot Digital Twin. Grant PID2023-149468NB-I00, funded by MCIU/AEI/10.13039/501100011033 and FEDER/EU