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A BOUNDARY AWARE NEURAL NETWORK FOR ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGERY [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Automatic road extraction from high-resolution remote sensing imagery has various applications like urban planning and automatic navigation. Existing methods for automatic road extraction however, focus on regional accuracy but not on the boundary ...
H. Sui, M. Zhou, M. Peng, N. Xiong
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DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation. Due to the long and thin shape as well as the shades induced by vegetation and
Ying Wang +6 more
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Remote Sensing, 2021 Automatic extraction of the road surface and road centerline from very high-resolution (VHR) remote sensing images has always been a challenging task in the field of feature extraction.
Zhenfeng Shao +3 more
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Remote Sensing, 2023 Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for ...
Seokchan Kang, Jeongwon Lee, Jiyeong Lee
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Remote Sensing, 2023 The accurate detection and extraction of roads using remote sensing technology are crucial to the development of the transportation industry and intelligent perception tasks.
Xiangzeng Liu +6 more
semanticscholar +1 more source
Remote Sensing, 2020 High-quality road network information plays a vital role in regional economic development, disaster emergency management and land planning. To date, studies have primarily focused on sampling flat urban roads, while fewer have paid attention to road ...
Zeyu Xu +7 more
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IEEE Access, 2023 As the boost of modern society, research on the extraction of road traffic markings has become increasingly popular. To improve the regional convolutional neural network, improve the road surface cloud segmentation algorithm based on the radius filtering
Fei Yu, Zhaoxia Lu
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AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Abstract. Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy.
P. Li, J. Feng, Z. Ma, X. Li, Yong Li
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Remote Sensing, 2023 Acquiring road information is important for smart cities and sustainable urban development. In recent years, significant progress has been made in the extraction of urban road information from remote sensing images using deep learning (DL) algorithms ...
Jingjing Tao +9 more
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LDANet: A Lightweight Dynamic Addition Network for Rural Road Extraction from Remote Sensing Images
Remote Sensing, 2023 Automatic road extraction from remote sensing images has an important impact on road maintenance and land management. While significant deep-learning-based approaches have been developed in recent years, achieving a suitable trade-off between extraction ...
Bohua Liu +4 more
semanticscholar +1 more source