This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …

Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

The foundation model has recently garnered significant attention due to its potential to
revolutionize the field of visual representation learning in a self-supervised manner. While …

Unsupervised pre-training methods for large vision models have shown to enhance
performance on downstream supervised tasks. Develo** similar techniques for satellite …

Large-scale vision foundation models have made significant progress in visual tasks on
natural images, with vision transformers (ViTs) being the primary choice due to their good …

Leveraging the extensive training data from SA-1B, the segment anything model (SAM)
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …

Deep learning approaches have contributed to the rapid development of remote sensing
(RS) image interpretation. The most widely used training paradigm is to use ImageNet …

In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

Deep learning has largely reshaped remote sensing (RS) research for aerial image
understanding and made a great success. Nevertheless, most of the existing deep models …

Change detection is an important tool for long-term Earth observation missions. It takes bi-
temporal images as input and predicts “where” the change has occurred. Different from other …