Instance segmentation is the state-of-the-art in object detection, and there are numerous
applications in remote sensing data where these algorithms can produce significant results …

Automatic extraction of building instances from high spatial resolution optical remote
sensing imagery is essential for urban infrastructure and smart management. In view of the …

Mapping new and old buildings are of great significance for understanding socio-economic
development in rural areas. In recent years, deep neural networks have achieved …

Building footprint generation is a vital task in a wide range of applications, including, to
name a few, land use management, urban planning and monitoring, and geographical …

Accurate and reliable building footprint maps are vital to urban planning and monitoring, and
most existing approaches fall back on convolutional neural networks (CNNs) for building …

Datasets play a key role in developing superior building detection approaches. However,
most of the previous work focuses on accurate building masks and scale expansion, while …

Building extraction from high-resolution remote sensing images plays a vital part in urban
planning, safety supervision, geographic databases updates, and some other applications …

To date, accurate building footprint delineation in the surveying, mapping, and geographic
information system (GIS) communities has been dependent on human labor. In this article, to …

Urban areas predominantly consist of complex building structures, which are assembled of
multiple building sections. From very high resolution remote sensing imagery, not only roof …

Automatic building extraction from high-resolution remote sensing images plays an
important role in many application fields, such as the urban planning and photogrammetry …