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 …

Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible
acquisition system that appears feasible for application in cadastral map**: high …

The application of the convolutional neural network has shown to greatly improve the
accuracy of building extraction from remote sensing imagery. In this paper, we created and …

Deep learning methods have achieved considerable progress in remote sensing image
building extraction. Most building extraction methods are based on Convolutional Neural …

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

Moving shadows need careful consideration in the development of robust dynamic scene
analysis systems. Moving shadow detection is critical for accurate object detection in video …

Recently, building segmentation (BS) has drawn significant attention in remote sensing
applications. Convolutional neural networks (CNNs) have become the mainstream analysis …

Identifying buildings from remote sensing imagery has been a challenge due to
uncertainties from remote sensing imagery and variations in building structure and texture …

High-resolution satellite imagery (HRSI) offers great possibilities for urban map**.
Unfortunately, shadows cast by buildings in high-density urban environments obscure much …

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 …