Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …

As the key part of autonomous driving (AD), High-Definition (HD) map provides more precise
location and rich semantic information than the traditional map. With the development of AD …

Road detection technology plays an essential role in a variety of applications, such as urban
planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there …

Mountain roads are a source of important basic geographic data used in various fields. The
automatic extraction of road images through high-resolution remote sensing imagery using …

In remote sensing, numerous unlabeled images are continuously accumulated over time,
and it is difficult to annotate all the data. Therefore, a self-supervised learning technique that …

In this study, we present a new automatic deep learning-based network named Road
Vectorization Network (RoadVecNet), which comprises interlinked UNet networks to …

Extracting roads from remote sensing images is of significant importance for automatic road
network updating, urban planning, and construction. However, various factors in complex …

Road information plays a fundamental role in application fields such as map updating, traffic
management, and road monitoring. Extracting road features from remote sensing images is …

Agricultural fields are essential in providing human beings with paramount food and other
materials. Quick and accurate identification of agricultural fields from the remote sensing …

Remote sensing (RS) scene classification is a challenging task to predict scene categories
of RS images. RS images have two main issues: large intraclass variance caused by large …