The extraction of urban structures such as buildings from very high-resolution (VHR) remote
sensing imagery has improved dramatically, thanks to recent developments in deep …

Unsupervised domain adaptation (UDA) is a weakly supervised learning technique that
classifies images in the target domain when the source domain has labeled samples, and …

Automatic building segmentation from satellite images is an important task for various
applications such as urban map**, disaster management and regional planning. With the …

Existing methods for building extraction from remotely sensed images strongly rely on aerial
or satellite-based images with very high resolution, which are usually limited by …

Semantic segmentation necessitates approaches that learn high-level characteristics while
dealing with enormous quantities of data. Convolutional neural networks (CNNs) can learn …

Weakly supervised semantic segmentation (WSSS) methods, utilizing only image-level
annotations, are gaining popularity for automated building extraction due to their …

Traditional approach from source image to application vectors in building extraction needs
additional complex regularization of converted intermediate raster results. While in …

In today's era, increasing access to very high-resolution remote sensing images (VHR-RSIs)
has enhanced building detection and change assessment capabilities. These applications …

The effective recognition and precise positioning of multiscale building rooftop is one of the
key scientific problems that have yet to be resolved urgently in the current implementation of …

Building footprint extraction is crucial for various applications, including disaster
management, change detection, and 3D modeling. Satellite and aerial images, when …