can you please help me to refine this abstract to make sure it is fitter a conference paper In our proposed model the weakly supervised learning network uses partially labeled samples for learning whi

Our proposed model leverages weakly supervised learning to use partially labeled samples for more efficient learning compared to fully supervised learning. We focus on the unique characteristics of satellite remote sensing images and divide them into small pieces to more effectively utilize the data and improve training efficiency. The model is trained on the large-scale BigEarthNet database, and a pixel-level image segmentation network is built to maximize the Multi Label features of the dataset. This enables more accurate pixel-level label maps for each patch, facilitating detailed pixel classification and efficient processing of remote sensing data for monitoring urban roads and flooding conditions. Our model has been successfully applied to Sentinel-1 and Sentinel-2 remote sensing images, demonstrating high accuracy and processing speed. Additionally, the model can automatically utilize additional information provided by Sentinel-1 data to process remotely sensed data at different scales. Experimental results indicate high accuracy and good generalization capability, providing a new approach for fast and accurate remote sensing data processing and contributing to the field of automated urban disaster warning. We believe the model has broad applicability to various remote sensing data processing fields