Remote Sensing Image Semantic Segmentation

Remote sensing image semantic segmentation aims to automatically classify each pixel in satellite or aerial imagery into meaningful categories, enabling detailed analysis of Earth's surface. Current research focuses on addressing challenges like variations in object scale and orientation, often employing advanced architectures such as transformers and state-space models alongside convolutional neural networks, and exploring techniques like unsupervised domain adaptation and federated learning to leverage diverse and potentially unlabeled datasets. This field is crucial for various applications, including environmental monitoring, urban planning, and precision agriculture, by providing accurate and efficient information extraction from large-scale imagery.