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

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 …

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 …

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 …

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 …

Building extraction is a challenging research direction in remote sensing image (RSI)
interpretation. Due to the fact that a building has not only its own local structures but also …

Rapidly developing remote sensing technology provides massive data for urban planning,
mapping, and disaster management. As a carrier of human productive activities, buildings …

Automatic building extraction from high-resolution aerial and satellite images has many
practical applications, such as urban planning and disaster management. However, the …

The complexity and diversity of buildings make it challenging to extract low-level and high-
level features with strong feature representation by using deep neural networks in building …