The rapid advancement of remote sensing technology has significantly enhanced the
temporal resolution of remote sensing data. Multitemporal remote sensing image …

Abstract Knowledge distillation is one effective approach to compress deep learning models.
However, the current distillation methods are relatively monotonous. There are still rare …

Building extraction by deep learning from remote sensing images is currently a research
hotspot. PSPNet is one of the classic semantic segmentation models and is currently …

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 …

Building footprint extraction in remote sensing remains challenging due to the diverse
appearances of buildings and confusing scenarios. Recently, researchers have revealed …

In practical applications, different application fields have various requirements regarding the
precision and completeness of building extraction. Too low precision or completeness may …

Detecting buildings, segmenting building footprints, and extracting building edges from high-
resolution remote sensing images are vital in applications such as urban planning, change …

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 …

Extracting building data from remote sensing images is an efficient way to obtain geographic
information data, especially following the emergence of deep learning technology, which …

Building height, as an essential measure of urban vertical structure, is key to understanding
how urbanization is reshaping inner-city characteristics, particularly in developing countries …