Zobrazeno 1 - 10
of 1 698
pro vyhledávání: '"road extraction"'
Publikováno v:
Geo-spatial Information Science, Pp 1-18 (2024)
Accurate and complete road network extraction plays a critical role in urban planning, street navigation, and emergency response. At present, narrow roads are a main feature in most public road datasets. However, the continuity and boundary completen
Externí odkaz:
https://doaj.org/article/efc6796467c84c3e9b2566fd97bc5d19
Publikováno v:
Geo-spatial Information Science, Pp 1-19 (2024)
Recent advancements in satellite remote sensing technology and computer vision have enabled rapid extraction of road networks from massive, Very High-Resolution (VHR) satellite imagery. However, current road extraction methods face the following limi
Externí odkaz:
https://doaj.org/article/d4538b27351d407885e29da57b1cae50
Publikováno v:
European Journal of Remote Sensing (2024)
Road detection in remote sensing (RS) images plays a critical role in applications ranging from urban planning to autonomous navigation systems. However, accurate road extraction remains a challenging task due to the presence of textual-similar objec
Externí odkaz:
https://doaj.org/article/9ddb479fd357414a836ae6eed7d29d9c
Publikováno v:
Complex & Intelligent Systems, Vol 10, Iss 3, Pp 4311-4328 (2024)
Abstract Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often o
Externí odkaz:
https://doaj.org/article/d49bd83838fc481695e2b360e6fe66ac
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTRoad extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional n
Externí odkaz:
https://doaj.org/article/3b45fea09df14694b6a0c6ae3d86fd05
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 16608-16624 (2024)
Most existing road extraction methods prioritize region accuracy at the expense of ignoring road boundaries and connectivity quality. The occluding objects such as buildings, trees, and vehicles in remote sensing data usually cause discontinuous mask
Externí odkaz:
https://doaj.org/article/f04585367f8e425bae07dda328a7ecea
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14194-14207 (2024)
Road segmentation is a crucial aspect in various fields, including intelligent transport systems and urban planning. This article proposes a solution to the problem of inaccurate road region extraction in small devices with limited resources. The pro
Externí odkaz:
https://doaj.org/article/1a36c9db201348e9b028002f1d91d9a3
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8358-8369 (2024)
Digital roads are a crucial component of smart city development and sustainable urban development, being one of the fundamental geographic elements. Remote sensing images and GPS trajectories are two important data sources for obtaining road informat
Externí odkaz:
https://doaj.org/article/625563778f094f69a427ff95b0f44e3a
Publikováno v:
IEEE Access, Vol 12, Pp 50300-50309 (2024)
To address the challenges of road extraction in high-resolution remote sensing images, this paper presents an enhanced UNet++ road extraction method that incorporates CBAM. The original UNet++ network is referenced, and the loss function is improved
Externí odkaz:
https://doaj.org/article/f50abf4e0f7641c7b7d721c1f734d351
Publikováno v:
IEEE Access, Vol 12, Pp 26550-26561 (2024)
The accurate extraction of railway tracks is crucial for the development of digital railway systems. However, traditional manual methods for track extraction are both time-consuming and tedious. At the same time, current deep learning neural networks
Externí odkaz:
https://doaj.org/article/0464c06b0b234312a31d920b63e64afb