Layered Scene-Aware Network: Fusing Multimodal Data for Fine-Grained Urban Functional Zone Classification

As the fundamental spatial units of urban areas, urban functional zones require accurate classification to support urban planning, spatial structure monitoring, and sustainable development research. However, existing studies have insufficiently explored scene-related features within multimodal data, and most research focuses solely on coarse-grained classification systems, making it difficult to achieve a refined understanding of internal urban spatial structures. To address this issue, this study proposes a Layered Scene-Aware Network (LSANet) for fine-grained urban functional zone classification. This network integrates scene visual features from remote sensing images, scene semantic features from point of interest (POI) textual information, and scene spatial features from POI spatial distribution information, constructing a multi-level representation of multimodal data correlation features. A dynamic attention mechanism is further introduced to enhance cross-modal interactions and improve classification accuracy and robustness. This study selected the area within Beijing's Sixth Ring Road as the case study. Results demonstrate that the proposed network achieves an overall accuracy of 85.76% and a Kappa coefficient of 0.8208 on the dataset. In addition, experiments on an independent Shanghai study area further validate the effectiveness and generalization capability of the proposed framework. Moreover, the scenario-based information complexity index constructed in this study can effectively measure the spatial heterogeneity and semantic diversity of different functional zones, providing methodological support for urban spatial cognition and refined governance.