Bi-branch Neural Network for Urban Functional Zone Mapping: Combining Remote Sensing Imagery and Point-of-Interest Data

Abstract. Urban functional zone (UFZ) classification is essential for understanding city dynamics, supporting urban planning, and enabling effective resource allocation. Traditional approaches rely heavily on remote sensing imagery, which often lacks the contextual information necessary for distinguishing between zones with similar visual features but different functions. This study proposes a novel multi-modal bi-branch deep learning model, named BibDL, which integrates remote sensing imagery with Point-of-Interest (POI) data for UFZ classification. The BibDL model leverages the complementary strengths of these data sources: remote sensing provides spatial and structural information, while POI data offers insights into human activities and land use patterns. Experimental results demonstrate that the BibDL model significantly outperforms a baseline model trained only on imagery, achieving higher F1 scores of 0.975 and Kappa coefficients of 0.953 across multiple UFZ categories. In particular, the BibDL model shows improved performance in challenging zones such as Commerce, Public, and Academia, which are often misclassified when using imagery alone. An ablation study highlights the substantial accuracy gains achieved by incorporating POI data, underscoring the value of a multi-modal approach for UFZ classification. The findings suggest that combining remote sensing imagery with contextual POI density image offers a powerful framework for more precise, context-aware UFZ classification, with implications for urban planning, smart city development, and sustainable resource management.