Extracting buildings and water bodies from high-resolution remote sensing images is of
great significance for urban development planning. However, when studying buildings and …

Dynamic monitoring of building environments is essential for observing rural land changes
and socio-economic development, especially in agricultural countries, such as China. Rapid …

There remains several challenges that are encountered in the task of extracting buildings
from aerial imagery using convolutional neural networks (CNNs). First, the tremendous …

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 …

An appropriate detection network is required to extract building information in remote
sensing images and to relieve the issue of poor detection effects resulting from the …

Building segmentation is crucial for applications extending from map production to urban
planning. Nowadays, it is still a challenge due to CNNs' inability to model global context and …

Crater recognition on Mars is of paramount importance for many space science applications,
such as accurate planetary surface age dating and geological mapping. Such recognition is …

Processing single high-resolution satellite images may provide a lot of important information
about the urban landscape or other applications related to the inventory of high-altitude …

Building extraction from remote sensing data is an important topic in urban studies and the
deep learning methods have an increasing role due to their minimal requirements in training …

In today's era, increasing access to very high-resolution remote sensing images (VHR-RSIs)
has enhanced building detection and change assessment capabilities. These applications …