Urban Functional Zone Classification via Advanced Multi-Modal Data Fusion

The classification of urban functional zones is crucial for improving land use efficiency and promoting balanced development across urban areas. Existing methods for classifying urban functional zones using mobile signaling data face challenges primarily due to the limitations of single data sources, insufficient utilization of multidimensional data, and inherent inaccuracies in mobile signaling data. To address these issues, this study proposes an innovative classification method that employs advanced multimodal data fusion techniques to enhance the accuracy and reliability of functional zone classification. Mobile signaling data are mapped into image data using timestamp and geographic location information and combined with point of interest (POI) data to construct a comprehensive multimodal dataset. Deep learning techniques are then applied to fuse the multimodal data features, enabling precise and reliable classification of functional zones. The experimental results demonstrate that this method achieves an accuracy of 95.128% in classifying urban functional zones, significantly outperforming methods that use single-modal data.