Building detection from very high resolution (VHR) optical remote sensing images, which is
an essential but challenging task in remote sensing, has attracted increased attention in …

Semantic change detection (SCD) refers to the task of simultaneously extracting changed
areas and their semantic categories (before and after the changes) in remote sensing …

High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

Image-to-image translation (I2I) aims to transfer images from a source domain to a target
domain while preserving the content representations. I2I has drawn increasing attention and …

Semantic change detection (SCD) extends the multiclass change detection (MCD) task to
provide not only the change locations but also the detailed land-cover/land-use (LCLU) …

Long-range contextual information is crucial for the semantic segmentation of high-
resolution (HR) remote sensing images (RSIs). However, image crop** operations …

Building footprint information is crucial for comprehending global urban development
processes. Deep learning algorithms have shown significant potential in building extraction …

Thanks to their capability of modeling global information, transformers have been recently
applied to change detection (CD) in remote sensing images. Generally, the changes in …

In recent years, the remote sensing image (RSI) semantic segmentation attracts increasing
research interest due to its wide application. RSIs are difficult to be processed holistically on …

The acceleration of urbanization and the increasing demand for precise city planning have
made the extraction of buildings and roads from remote sensing images crucial. Deep …