A Method for Segmentation and Identification of Urban Functional Areas with Generalization Ability Based on Scale-Aware Feature-Enhanced Attention Mask R-CNN

Accurate identification of urban functional zones is critically important for urban planning and sustainable development. However, existing deep learning methods exhibit limited cross-city generalization capabilities, rendering them inadequate for large-scale automated mapping applications. To address this challenge, this study proposes a cross-city identification framework based on SFA-Mask R-CNN (Scale-Aware Feature-enhanced Attention Mask R-CNN), which achieves precise segmentation and classification of functional zones by integrating three complementary feature enhancement mechanisms: a Convolutional Block Attention Module (CBAM) for discriminative feature recalibration, a novel Scale-Aware FPN (SA-FPN) that dynamically adjusts feature pyramid layer weights according to the scale distribution of input imagery to improve cross-city transferability, and an ASPP (Atrous Spatial Pyramid Pooling) module for multi-scale contextual feature extraction, combined with multi-source data fusion. Using three cities along the Yangtze River Economic Belt—Chengdu, Wuhan, and Shanghai—as study areas, we systematically evaluated the model’s cross-city generalization performance. The results show that the Wuhan model exhibits the strongest cross-city transferability, achieving 85.37% accuracy in Chengdu, suggesting that models trained on cities at transitional development stages possess superior generalizability. In contrast, the gradient reversal layer-based domain adaptation approach tested in this study failed to effectively enhance model performance. This study provides a practical technical pathway for “train once, apply to multiple cities” large-scale functional zone mapping and offers new perspectives for research on regional disparities in urban development.