Satellite Image Segmentation

Satellite image segmentation aims to automatically partition satellite images into meaningful regions, such as buildings, roads, or vegetation, facilitating analysis for various applications. Current research emphasizes improving segmentation accuracy through advanced deep learning architectures, including transformers and U-nets, often leveraging techniques like transfer learning and data augmentation (e.g., using generative models to synthesize training data) to address data scarcity and domain adaptation challenges. These advancements are crucial for diverse fields, including climate change monitoring, urban planning, and precision agriculture, by enabling efficient and accurate extraction of information from vast amounts of satellite imagery.