Building type prediction is a critical task for urban planning and population estimation. The
growing availability of multi-source data presents rich semantic information for building type …

Automatically extracting buildings from remote sensing images with deep learning is of great
significance to urban planning, disaster prevention, change detection, and other …

Unplanned urban settlements exist worldwide. The geospatial information of these areas is
critical for urban management and reconstruction planning but usually unavailable …

Having accurate building information is paramount for a plethora of applications, including
humanitarian efforts, city planning, scientific studies, and navigation systems. While …

Accurate delineation of urban form is essential to understand the impacts that urbanization
has on the environment and regional climate. Conventional supervised classification of …

Building usage maps are inputs in various urban applications. Although Street View Images
(SVIs) are applied in many studies, their usage in the generation of building usage maps is …

As deep learning technology advances and more urban spatial-temporal data accumulates,
an increasing number of deep learning models are being proposed to solve urban spatial …

A great number of studies for identification and localization of buildings based on remote
sensing data has been conducted over the past few decades. The majority of the more …

Rapid urban growth and globalization affect land use in cities, and the need for automatic
interpretation of remote sensing images is constantly increasing. Deep neural networks are …

Extracting and recognizing buildings from high-resolution remote sensing images faces
many problems due to the complexity of the buildings on the surface. The purpose is to …