Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

The annotations used during the training process are crucial for the inference results of
remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be …

There are various binary semantic segmentation tasks in remote sensing (RS) that aim to
extract the foreground areas of interest, such as buildings and roads, from the background in …

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity
assessment, and forest resource management. In the realm of forest cover mapping …

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 …

Recently, remote sensing image (RSI) semantic segmentation technology has advanced
greatly, with the fully supervised process achieving particularly strong performance …

Remote sensing (RS) image semantic segmentation has attracted much attention due to its
wide applications. However, deep learning-based RS image semantic segmentation …

Arctic Permafrost is facing significant changes due to global climate change. As these
regions are largely inaccessible, remote sensing plays a crucial rule in better understanding …

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 …

Semantic understanding of high-resolution remote sensing (RS) images is of great value in
Earth observation, however, it heavily depends on numerous pixel-wise manually-labeled …