Rivers, as fundamental components of freshwater supply and wetland ecosystems, play an essential role in sustaining biodiversity and facilitating sustainable resource utilization. This study introduces the integration of the attention mechanism within the convolutional neural network (CNN) framework and constructs seven enhanced models. A novel dataset has been independently developed utilizing high spatial resolution remote sensing images obtained from China’s Gaofen-2 satellite (GF-2), which enables the efficient and precise extraction of river distribution. The city of Zhuhai, characterized by its intricate river network located in the lower reaches of the Pearl River Basin, has been selected as the experimental area for this research. The experimental results indicate that the CNN model enhanced by the attention mechanism significantly surpasses the baseline model across several performance metrics, including overall accuracy, Kappa coefficient, Precision, Recall, F1-score, Mean Intersection over Union, and the extraction result map. Notably, the model incorporating the Bottleneck Attention Module demonstrates the highest performance, achieving overall accuracy and Kappa coefficient values of 93.09% and 0.8618, respectively, which surpass the baseline model by 12.62% and 0.2524. This study thus provides crucial spatial data and method support for river resource management, supporting ecological conservation and sustainable wetland management.

Remote sensing land-cover classification is impeded by limited annotated data and pronounced geometric distortion, hindering its value for environmental monitoring and land planning. We introduce LVC2‑DViT (Landview Creation for Landview Classification with Deformable Vision Transformer), an end‑to‑end framework evaluated on five Aerial Image Dataset (AID) scene types, including Beach, Bridge, Pond, Port and River. LVC2‑DViT fuses two modules: (i) a data creation pipeline that converts ChatGPT-4o-generated textual scene descriptions into class‑balanced, high-fidelity images via Stable Diffusion, and (ii) DViT, a deformation‑aware Vision Transformer dedicated to land‑use classification whose adaptive receptive fields more faithfully model irregular landform geometries. Without increasing model size, LVC2‑DViT improves Overall Accuracy by 2.13 percentage points and Cohen’s Kappa by 2.66 percentage points over a strong vanilla ViT baseline, and also surpasses FlashAttention variant. These results confirm the effectiveness of combining generative augmentation with deformable attention for robust land‑use mapping. The project is available at here.