Road high-precision mobile LiDAR measurement point clouds are the digital infrastructures for maps, autonomous driving, twins, etc. High-precision automated semantic segmentation of road is a crucial research direction. Aiming at problem low accuracy existing deep learning networks inhomogeneous sparse system measurements (MLS), method that adaptively adjusts sampling radius region groups accor...

Road boundary estimation is an essential task for autonomous vehicles and intelligent driving assistants. It considerably straightforward to attain the when roads are marked properly with indicators. However, estimating road reliably without prior knowledge of road, such as markings, extremely difficult. This paper proposes a method estimate boundaries in different environments deep learning-ba...

Acquiring road information is important for smart cities and sustainable urban development. In recent years, significant progress has been made in the extraction of from remote sensing images using deep learning (DL) algorithms. However, due to complex shape, narrowness, high span roads images, results are often unsatisfactory. This article proposes a Seg-Road model improve connectivity. The us...

Urban road tunnels provide an increasingly cost-effective engineering solution, especially in compact cities like Singapore. For some urban road tunnels, tunnel characteristics such as tunnel configurations, geometries, provisions of tunnel electrical and mechanical systems, traffic volumes, etc. may vary from one section to another. These urban road tunnels that have characterized nonuniform p...

Based on an analysis of the current research and application of Road maintenance, geographic information system (WebGIS) and ArcGIS Server, the platform overhead construction for Road maintenance development is studied and the key issues are presented, including the organization and design of spatial data on the basis of the geodatabase technology, middleware technology, tiles cache index techn...

Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model is two-fold: firs...

Road identification from high-precision images is important to programmed mapping, urban planning, and updating geographic information system (GIS) databases. Manual of roads slow, costly, prone errors. Therefore, it a hot topic among remote sensing experts develop techniques for road satellite images. The main challenge lies in the variation width surface contents between roads. This paper pre...