D-Net:一种动态模拟道路形态的路网提取模型OACSTPCD

In the current road extraction from remote sensing images using deep learning techniques,the issue arises due to the use of rectangular convolutions in the network,leading to difficulties in capturing road morphology features and low extraction accuracy.We proposed a convolu-tional neural network based on deformable convolution(D-Net),aiming to accurately extract road areas from remote sensing images.To validate the model's performance,we conducted experiments on the road datasets of Massachusetts and DeepGlobe.Additionally,we chose a GF-2 satel-lite image to evaluate the model's generalization ability.Experimental results demonstrate that D-Net significantly improves road segmentation accuracy,providing strong support for optimizing extraction results through deformable convolutions.The progress of this research holds signifi-cant theoretical and practical implications for further exploration in intelligent road extraction.