Design of VGG Structured U-Net Model for Remote Sensing Green Space Information Extraction

With the intensification of urbanization, urban green spaces are of great significance for maintaining ecological balance. However, traditional methods for extracting green space information have problems such as insufficient accuracy and slow processing speed. Therefore, the study constructed a dataset using high-resolution remote sensing images collected by drones. A visual geometric group network structure U-shaped network model for remote sensing green space information extraction was proposed, and an improved activation function was designed. The research model was experimentally validated. The experiment outcomes showed that the proposed model had a fast convergence speed, converging after 10 iterations with a loss value of only 0.024, an overall accuracy of 98.54%, and an average Kappa coefficient of 0.921. In practical application testing, the proposed model showed excellent performance, prominent effect, minimal information loss, and overall precision remained above 97%. In addition, the green space information extraction rate of the proposed model exceeded 95.00%, with an average of 96.97%, far superior to traditional methods. This research provides new technological means for monitoring and managing urban green spaces, which helps promote the growth of remote sensing information extraction technology and is significant for urban planning, ecological environment protection, and sustainable development.