One of the most challenging research subjects in remote sensing is feature extraction, such
as road features, from remote sensing images. Such an extraction influences multiple …

Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …

One of the most important tasks in the advanced transportation systems is road extraction.
Extracting road region from high-resolution remote sensing imagery is challenging due to …

Roads are important mode of transportation, which are very convenient for people's daily
work and life. However, it is challenging to accuratly extract road information from a high …

Road extraction from remote sensing imagery is a fundamental task in the field of image
semantic segmentation. For this goal, numerous supervised deep learning techniques have …

Recent advances in deep-learning methods have shown extraordinary performance in road
extraction from high resolution satellite imagery. However, most existing deep-learning …

Extracting buildings and roads from remote sensing images is very important in the area of
land cover monitoring, which is of great help to urban planning. Currently, a deep learning …

These days, extracting information from remote sensing data has a great impact on various
aspects of our lives, such as infrastructure and urban planning, transportation and traffic …

Road network extraction from remotely sensed imagery has become a powerful tool for
updating geospatial databases, owing to the success of convolutional neural network (CNN) …

Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and
satellite (or high-resolution, HR) images, has been applied to many application domains …