Remote sensing (RS) images enable high-resolution information collection from complex
ground objects and are increasingly utilized in the earth observation research. Recently, RS …

Long-range dependency modeling has been widely considered in modern deep learning-
based semantic segmentation methods, especially those designed for large-size remote …

Remote sensing is stepping into the era of the foundation model, where the fine-tuning
paradigm is widely adopted to transfer the profound knowledge of pretrained foundation …

Semantic segmentation is one of the most important tasks in remote sensing. In the semantic
segmentation of remote sensing images, some regions are repeatedly transformed between …

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as
dense prediction task for HRS image, has been and will continue to be important research in …

For remote sensing image segmentation, the boundaries of objects are difficult to
distinguish, which is ignored by most methods. Therefore, it is challenging how to excavate …

Semantic segmentation is an essential technique in remote sensing. Until recently, most
related research has focused primarily on advancing semantic segmentation models based …

In the context of fast progress in deep learning, convolutional neural networks have been
extensively applied to the semantic segmentation of remote sensing images and have …

Recently, several land-cover classification models have achieved great success in terms of
both accuracy and computational performance. However, it remains challenging due to …

Current deep learning methods for semantic segmentation in remote sensing heavily
depend on a substantial amount of labeled data. However, obtaining pixel-level labeled …