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 …

Abstract Knowledge distillation is one effective approach to compress deep learning models.
However, the current distillation methods are relatively monotonous. There are still rare …

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 …

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 …

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 …

Deep learning techniques have been applied to extract road areas from remote sensing
images, leveraging their efficient and intelligent advantages. However, the contradiction …

Topological information is a crucial factor affecting road feature extraction using semantic
segmentation. Many segmentation models have recently been developed for road feature …

In recent years, the integration of deep learning technology based on convolutional neural
networks with historical maps has made it possible to automatically extract roads from these …

Urban road extraction is important for the applications of urban planning and transportation.
High-resolution image (HRI) has been one of the most popular data sources for extracting …

Rapid progress has been made in the research of high-resolution remote sensing road
extraction tasks in the past years but due to the diversity of road types and the complexity of …