Predicting housing price, housing density, and green area coverage from combined satellite and street view imagery using deep learning

Abstract Rapid urban expansion and rising housing prices have created significant social and economic challenges in many cities, exposing the limitations of traditional, resource-intensive data collection methods for urban planning. Remote sensing technologies, such as satellite imagery, offer a cost-effective alternative for data collection. Incorporating street view imagery can enhance the quality of collected information by providing a human perspective on the urban environment, often inaccessible through standard remote sensing methods. This research aimed to develop a deep learning classification model using a multi-modal fusion of satellite and street view imagery to predict urban metrics of housing price, housing density, and green area coverage. Focusing on Lisbon, a European city facing rising housing prices, this research used EfficientNetB0, a pre-trained model originally used for object recognition on the ImageNet dataset, which successfully generalized its learning to interpret urban imagery. The findings highlight the potential of integrating pre-trained models within deep learning frameworks for urban analysis. This approach leverages low-cost, readily available data, providing a scalable alternative to traditional methods and a foundation for developing predictive tools for urban metrics.