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

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …

Road segmentation from remote-sensing images is a challenging task with wide ranges of
application potentials. Deep neural networks have advanced this field by leveraging the …

Automatic remote sensing tools can help inform many large-scale challenges such as
disaster management, climate change, etc. While a vast amount of spatio-temporal satellite …

Extracting roads from satellite imagery is a promising approach to update the dynamic
changes of road networks efficiently and timely. However, it is challenging due to the …

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 …

Road extraction in remote sensing images is of great importance for a wide range of
applications. Because of the complex background, and high density, most of the existing …

Although multiple learning-based crack detection systems show promising results in
detecting cracks with pixel accuracy on individual images, few effectively enable inspection …

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 …

Automatic road extraction from high-resolution remote sensing imagery has various
applications like urban planning and automatic navigation. Existing methods for automatic …