Automated extraction of buildings from Earth observation (EO) data is important for various
applications, including updating of maps, risk assessment, urban planning, and policy …

Automated extraction of buildings from earth observation (EO) data has long been a
fundamental but challenging research topic. Combining data from different modalities (eg …

The use of deep learning methods to extract buildings from remote sensing images is a key
contemporary research focus, and traditional deep convolutional networks continue to …

In recent years, significant progress has been made in extracting buildings from high spatial
resolution (HSR) remote sensing images due to the rapid development of deep learning …

Automatic building extraction has been applied in many domains. It is also a challenging
problem because of the complex scenes and multiscale. Deep learning algorithms …

Building extraction refers to the automatic identification and separation of buildings from the
background in remote sensing images. It plays a significant role in urban planning, land …

Building extraction from remote sensing images is extremely important for urban planning,
land-cover change analysis, disaster monitoring, and so on. With the growing diversity in …

Automated extraction of buildings from remotely sensed data is important for a wide range of
applications but challenging due to difficulties in extracting semantic features from complex …

Deep learning approaches have been widely used in building automatic extraction tasks
and have made great progress in recent years. However, the missing detection and wrong …

Deep learning (DL) shows remarkable performance in extracting buildings from high
resolution remote sensing images. However, how to improve the performance of DL based …