In recent years, AI and deep learning (DL) methods have been widely used for object
classification, recognition, and segmentation of high-resolution multispectral remote sensing …

Deep learning algorithms offer an effective solution to the inefficiencies and poor results of
traditional methods for building a footprint extraction from high-resolution remote sensing …

Built-up areas and buildings are two main targets in remote sensing research; consequently,
automatic extraction of built-up areas and buildings has attracted extensive attention. This …

It is challenging for semantic segmentation of buildings based on high-resolution remote
sensing images, given high variability of appearance and complicated backgrounds of the …

In recent years, convolutional neural networks (CNNs) have been widely used in extracting
buildings from remote sensing images. Both semantic representation and spatial location …

To solve the problems of low utilization of spatial features and incomplete contour
segmentation in building semantic segmentation of remote sensing images, a building …

The traditional deep learning networks in remote sensing image building segmentation have
problems such as incomplete internal extraction, low accuracy of edge segmentation, and …

The accurate detection and mapping of buildings from high-resolution remote sensing
(HRRS) images have attracted extensive attention. However, as an artificial target, buildings …

Building extraction from very high resolution (VHR) imagery plays an important role in urban
planning, disaster management, navigation, updating geographic databases, and several …

With the rapid development of Earth observation technology and deep learning, building
extraction from remotely sensed imagery based on deep convolutional neural networks has …