Satellite Image Segmentation using U-Net.

Abstract

Satellite image segmentation plays a crucial role in remote sensing tasks like land cover classification, urban planning, and environmental monitoring. Deep learning models, particularly U-Net architectures, have become popular for achieving pixel-wise classification in high-resolution satellite images. By training on labeled datasets and using techniques like data augmentation and hyperparameter tuning, these models can reach high segmentation accuracy, with IoU scores around 0.8. Continued research focuses on improving generalization through methods like attention mechanisms and multi-scale feature fusion.