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.

Accurate mapping of rubber plantations is essential for sustainable land management and forest conservation in tropical regions. However, existing methods face two major challenges: persistent cloud cover limits the effectiveness of optical remote sensing in tropical areas, and regional phenological heterogeneity hinders the transferability of fixed-parameter approaches. This study proposes a Phenology-Adaptive Rubber Plantation Mapping (PARM) framework that integrates Sentinel-1 SAR time-series data with optimally selected spectral indices through a cascading constraint architecture. The framework operates as a structurally coherent system wherein SAR-derived phenological anchors explicitly govern downstream optical analysis across three internally dependent modules. First, three key phenological nodes—leaf-off start (LOS), fastest greening point (FGP), and full canopy point (FCP)—are extracted directly from SAR VH-polarization backscatter time series, enabling cloud-independent extraction of phenological temporal anchors. Second, the Jeffries–Matusita (JM) distance, evaluated within SAR-constrained phenological windows, is employed to identify the optimal vegetation and water indices for each region from six candidate spectral indices. Third, a time-weighted Rubber Plantation Discrimination Index (RPDI) is constructed using the selected indices and locally extracted phenological nodes, thereby amplifying the coupled signals of canopy greenness and moisture dynamics during critical phenological transitions. The framework was validated in Hainan Island and Vietnam, two regions with contrasting phenological regimes, using a spatial-block partitioning protocol (leave-one-subregion-out combined with DBSCAN-based clustering) designed to prevent samples from the same plantation from occurring in both training and test subsets. Within the Dynamic World forest mask, PARM achieved overall accuracies of 92.04% and 91.24%, respectively (93.57% and 91.00% on the fully held-out Qionghai City and Gia Lai province subregions), with Kappa coefficients exceeding 0.81 in both regions, consistently outperforming schemes based on raw spectral bands, individual spectral indices or direct multi-source time-series stacking. Error structure analysis revealed that residual classification failures are primarily associated with landscape fragmentation, stand immaturity, and residual cloud contamination, delineating the generalizability boundaries of the framework. These results demonstrate that tightly coupling SAR-based phenological characterization with adaptive optical index selection through a cascading constraint architecture provides a reliable foundation for rubber plantation mapping in cloud-prone tropical regions.