Remote sensing image semantic segmentation (RSISS) remains challenging due to the
scarcity of labeled data. Semisupervised learning can leverage pseudolabels to enhance …

Building 3-D reconstruction from remote sensing images has a wide range of applications in
smart cities, photogrammetry, and other fields. Methods for automatic 3-D urban building …

Deep-learning-based (DL) methods have dominated the task of semantic segmentation of
remote sensing images. However, the sizes of different objects vary widely, and there is a …

Semantic segmentation of remote sensing images is vital in remote sensing technology.
High-quality models for this task require a vast amount of images, and manual annotation is …

In the field of remote sensing technology, the semantic segmentation of remote sensing
images carries substantial importance. The creation of high-quality models for this task calls …

Abstract The Segment Anything Model (SAM) exhibits remarkable versatility and zero-shot
learning abilities, owing largely to its extensive training data (SA-1B). Recognizing SAM's …

Semantic segmentation of high spatial resolution (HSR) remote sensing images presents
unique challenges due to the imbalanced foreground–background distribution and large …

Semantic segmentation of aerial images is crucial yet resource-intensive. Inspired by human
ability to learn rapidly, few-shot semantic segmentation offers a promising solution by …

Thanks to the application of deep learning technology in point cloud processing of the
remote sensing field, point cloud segmentation has become a research hotspot in recent …

Contextual information can effectively aid deep-learning models in extracting interclass and
intraclass difference features in remote sensing images. This article presents a novel …