High Resolution Remote Sensing

High-resolution remote sensing focuses on extracting detailed information from very high-resolution imagery, primarily aiming for accurate and efficient object detection, segmentation, and change detection. Current research emphasizes developing novel deep learning architectures, such as UNet and Transformer-based models, often incorporating techniques like attention mechanisms and contrastive learning to improve accuracy and efficiency, particularly in challenging scenarios with limited labeled data or large image sizes. This field is crucial for various applications, including urban planning, disaster management, and environmental monitoring, providing timely and detailed insights for improved decision-making and resource allocation.