Object detection in optical remote sensing images, being a fundamental but challenging
problem in the field of aerial and satellite image analysis, plays an important role for a wide …

Ideal for both undergraduate and graduate students in the fields of geography, forestry,
ecology, geographic information science, remote sensing, and photogrammetric …

The extraction of urban structures such as buildings from very high-resolution (VHR) remote
sensing imagery has improved dramatically, thanks to recent developments in deep …

Building extraction from high-resolution satellite images (HRSI) in urban areas is an intricate
problem. Recent studies proposed different methods during 2005–2015. However, in HRSI …

Semantic segmentation is a significant method in remote sensing image (RSIs) processing
and has been widely used in various applications. Conventional convolutional neural …

Semantic segmentation of high-resolution remote sensing images plays an important role in
applications for building extraction. However, the current algorithms have some semantic …

Image matching based on local invariant features is crucial for many photogrammetric and
remote sensing applications such as image registration and image mosaicking. In this …

TEMPORARY REMOVAL: Aerial imagery for roof segmentation: A large-scale dataset towards
automatic mapping of buildings - NASA/ADS Now on home page ads icon ads Enable full …

Convolutional neural networks show excellent performance in image segmentation.
However, compared with natural images, remote sensing images are characterized by large …

In this study, we propose a novel methodology for automated detection of buildings from
single very-high-resolution (VHR) multispectral images. The methodology uses the principal …