Road extraction from high-resolution remote sensing images has long been a focal and
challenging research topic in the field of computer vision. Accurate extraction of road …

Transportation network extraction based on deep learning has become a hotspot. However,
the existing models all aim to distinguish between background and transportation networks …

This paper focuses on crack detection and digital inpainting of ancient fresco cultural
heritage, and proposes a method to use diffusion model for fresco inpainting. It effectively …

The vector information of mountainous roads is great significance, including regional
economics, emergency response, disaster management, among others. Nevertheless, there …

The extraction of urban road features provides indispensable support to numerous high-
accurate applications such as autonomous driving and urban high-definition mapping …

Road extraction plays a crucial role in various sectors, including transportation systems,
disaster relief distribution, and urban planning, necessitating a more efficient method than …

Road extraction from remote sensing images has attracted widespread attention of
researchers due to its crucial role in the fields of autopilot, urban planning, navigation and …

In this paper, we propose a robust and efficient end-to-end model named UWDepth for fast
monocular depth estimation that incorporates underwater domain knowledge into it as a …

Road extraction from remote sensing images has gradually become a prominent research
hotspot in the field of autonomous driving and smart city construction. In recent years, with …

In this paper, an adaptive modulation and coding (AMC) scheme is proposed that aims to
maximize the energy efficiency of a single link by jointly scheduling the coding rate …