Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …

We present SegNeXt, a simple convolutional network architecture for semantic
segmentation. Recent transformer-based models have dominated the field of se-mantic …

Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …

In recent years semantic segmentation has become a pivotal tool in processing and
interpreting satellite imagery. Yet a prevalent limitation of supervised learning techniques …

Earth observation is a fundamental tool for monitoring the evolution of land use in specific
areas of interest. Observing and precisely defining change, in this context, requires both time …

Geospatial object segmentation, a fundamental Earth vision task, always suffers from scale
variation, the larger intra-class variance of background, and foreground-background …

When performing remote sensing image segmentation, practitioners often encounter various
challenges, such as a strong imbalance in the foreground–background, the presence of tiny …

Semantic segmentation of remote sensing images aims to assign a specific label or class to
each pixel in an image, which plays an extremely important role in scene understanding …

There are still various challenges in remote sensing semantic segmentation due to objects
diversity and complexity. Transformer-based models have significant advantages in …

Remote sensing semantic segmentation plays a significant role in various applications such
as environmental monitoring, land use planning, and disaster response. Convolutional …