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

Road information from high-resolution remote-sensing images is widely used in various
fields, and deep-learning-based methods have effectively shown high road-extraction …

Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide
variety of applications, such as autonomous driving, path planning, and road navigation …

Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to
the diverse landscapes and different sizes of geo-objects that RSI contains, making …

Roads are the fundamental elements of transportation, connecting cities and rural areas, as
well as people's lives and work. They play a significant role in various areas such as map …

With geospatial intelligence research developing rapidly, automatic road extraction is
becoming a fundamental and challenging task. Due to the special geometric structure and …

Efficient and accurate road extraction from remote sensing imagery is important for
applications related to navigation and Geographic Information System updating. Existing …

The accurate detection and extraction of roads using remote sensing technology are crucial
to the development of the transportation industry and intelligent perception tasks. Recently …

The past several years have witnessed the rapid development of the task of road extraction
in high-resolution remote sensing images. However, due to the complex background and …

Road networks in the past are imperative for understanding evolution of transportation
infrastructure, urban sprawl, and route planning, etc. Various approaches have been …