Building extraction from remote sensing (RS) images is a fundamental task for geospatial
applications, aiming to obtain morphology, location, and other information about buildings …

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 …

As an important carrier of human productive activities, the extraction of buildings is not only
essential for urban dynamic monitoring but also necessary for suburban construction …

Building footprint information is one foundation for understanding urban processes and
hence a program for environmentally sustainable urbanization. For most cities, municipal …

Extracting building footprints from remotely sensed imagery has long been a challenging
task and is not yet fully solved. Obstructions from nearby shadows or trees, varying shapes …

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 …

Deep learning based methods have significantly boosted the study of automatic building
extraction from remote sensing images. However, delineating vectorized and regular …

Deep learning methods based upon convolutional neural networks (CNNs) have
demonstrated impressive performance in the task of building outline delineation from very …

Image processing via convolutional neural network (CNN) has been developed rapidly for
remote sensing technology. Moreover, techniques for accurately extracting building …