The data-complete and detail-correct road network information serves as important evidence
in numerous transportation-associated applications. Regular and rapid road network …

Abstract The Extraction of Roads from Remote Sensing Imagery is a rapidly developing field
that has significant impacts on both the economic and social domains. In the fields of urban …

Road extraction from remote sensing (RS) images in very high resolution is important for
autonomous driving and road planning. Compared with large-scale objects, roads are …

Remote sensing object detection (RSOD) encounters challenges in effectively extracting
features of small objects in remote sensing images (RSIs). To alleviate these problems, we …

Segmentation of a road portion from a satellite image is challenging due to its complex
background, occlusion, shadows, clouds, and other optical artifacts. One must combine both …

Roads are the fundamental elements of transportation, connecting cities and rural areas, as
well as people's lives and work. They play a significant role in various areas such as map …

It is a challenging task to segment drivable area of road in automatic driving system.
Convolutional neural network has excellent performance in road segmentation. However …

Building segmentation is an important step in urban planning and development. In this work,
we propose a new deep learning model, namely Multidimension Attention U‐Net (MDAU …

Instance segmentation of pavement crack presents notable challenges but is pivotal for
practical applications such as crack visual tracking and automated crack repair. However …

In the realm of high-resolution remote sensing image (HRSI) segmentation, convolutional
neural networks have shown their effectiveness and superiority. However, there are still two …