Abstract Three-dimensional (3D) building structures are vital to understanding urban
dynamics. Monocular remote sensing imagery is a cost-effective data source for large-scale …

Building footprint extraction is a basic task in the fields of mapping, image understanding,
computer vision, and so on. Accurately and efficiently extracting building footprints from a …

The application of convolutional neural networks has been shown to significantly improve
the accuracy of building extraction from very high-resolution (VHR) remote sensing images …

Establishing up-to-date large scale building maps is essential to understand the urban
dynamics, such as estimating population, urban planning, and many other applications …

In the remote sensing community, extracting buildings from remote sensing imagery has
triggered great interest. While many studies have been conducted, a comprehensive review …

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 …

Roof structure information is essential for creating detailed 3D building models. These serve
in many applications that require knowledge of the roof type and geometry. Automated …

In the field of building extraction, many convolutional neural network (CNN)-based methods
have been developed to solve the problem of the irregular boundaries in their predictions …

Buildings constitute one of the most important landscapes in remote sensing (RS) images
and have been broadly analyzed in a wide range of applications from urban planning to …

The automatic extraction of building outlines from high-resolution images is an important
and challenging task. Convolutional neural networks have shown excellent results …