Automated extraction of polygonal building contours from high-resolution remote sensing
images is important for various applications. However, it remains a difficult task to achieve …

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 …

Remote sensing change detection (CD) is used to detect the difference in the state of objects
or phenomena by observing it at different times. CD is widely used in disaster monitoring …

Remote sensing images can have significant appearance differences due to various factors,
such as atmospheric conditions, sensor types, seasons, and capture times. Therefore, when …

The local climate zones (LCZs) classification system has emerged as a more refined method
for assessing the urban heat island (UHI) effect. However, few researchers have conducted …

The building is a critical urban element for developing smart cities. Building extraction
research has experienced rapid development due to abundant remote sensing image data …

Agricultural parcel vectorization is important for precision agriculture when analyzing crops
at the parcel scale. However, it is a challenging task to vectorize parcels from satellite …

In this study, using deep learning-based semantic segmentation methods, an automatic
building segmentation application was carried out with a remote sensing image on a sample …

Building extraction from remote sensing imagery has been a research hotspot for some time
with the advancement of AI in remote sensing. However, the edges of buildings extracted …

Automatic building instance extraction from high-resolution (HR) remote sensing imagery
(RSI) is crucial for urban planning and mapping. The dominant approaches are based on …