The applied research in remote sensing images has been pushed by convolutional neural
network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model …

Semantic segmentation methods based on deep neural networks have achieved great
success in recent years. However, training such deep neural networks relies heavily on a …

Semantic segmentation is an extremely challenging task in high-resolution remote sensing
(HRRS) images as objects have complex spatial layouts and enormous variations in …

Green plastic cover (GPC) is a kind of green plastic fine mesh primarily used for covering
construction sites and mitigating large amounts of dust during construction. Accurate GPC …

Traditional building and water segmentation methods are vulnerable to noise interference,
and hence, they could not avoid missed and false detections in the detection process …

The foundation model (FM) has garnered significant attention for its remarkable transfer
performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a …

Semantic segmentation of remote sensing images (SSRSIs), which aims to assign a
category to each pixel in remote sensing images, plays a vital role in a broad range of …

For visual question answering on remote sensing (RSVQA), current methods scarcely
consider geospatial objects typically with large-scale differences and positional sensitive …

Semantic segmentation based on deep learning has achieved impressive results in recent
years, but these results are supported by a large amount of labeled data, which requires …

Boundary information is essential for the semantic segmentation of remote sensing images.
However, most existing methods were designed to establish strong contextual information …