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 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 …

Contrast to the global forest, few trees live in cities but contribute significantly to urban
environment and human health. However, the classical satellite-derived land cover/forest …

Automatic road extraction from remote sensing images plays an important role for
navigation, intelligent transportation, and road network update, etc. Convolutional neural …

DBSCAN is the most famous density based clustering algorithm which is one of the main
clustering paradigms. However, there are many redundant distance computations among …

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

Road extraction from optical remote sensing images has many important application
scenarios, such as navigation, automatic driving and road network planning, etc. Current …

Counting the trees in a given area or city is meaningful when making decisions for
government policies and administration, including the international afforestation effort (ie …

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 …

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