Results 21 to 30 of about 2,255,908 (239)
Exploring multiple crowdsourced data to learn deep convolutional neural networks for road extraction
International Journal of Applied Earth Observations and Geoinformation, 2021 Road extraction from high-resolution remote sensing images (HRSIs) is essential for applications in various areas. Although deep convolutional neural networks (DCNNs) have exhibited remarkable success in road extraction, the performance relies on a large
Panle Li +11 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, 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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Dual-Task Network for Road Extraction From High-Resolution Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 In high-resolution remote sensing images, road scale diversity and occlusions caused by shadows, buildings, and vegetation often pose challenges for road extraction.
Yuzhun Lin +4 more
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Road Extraction in SAR Images Using Ordinal Regression and Road-Topology Loss
Remote Sensing, 2021 The road extraction task is mainly composed of two subtasks, namely, road detection and road centerline extraction. As the road detection task and road centerline extraction task are strongly correlated, in this paper, we introduce a multitask learning ...
Xiaochen Wei, Xiaolei Lv, Kaiyu Zhang
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Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images
Applied Sciences, 2022 Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-
Kaili Yang +5 more
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ROBUST APPROACH FOR URBAN ROAD SURFACE EXTRACTION USING MOBILE LASER SCANNING 3D POINT CLOUDS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity.
A. Nurunnabi +4 more
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ROAD EXTRACTION FROM HIGH-RESOLUTION SATELLITE IMAGES: CHALLENGES, SOLUTIONS, AND FUTURE DIRECTIONS [PDF]
Proceedings on Engineering SciencesRoad extraction is a widespread use of remote sensing images; however, extracting roads precisely and effectively from satellite images is challenging.
Prerna Rawat +2 more
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RemainNet: Explore Road Extraction from Remote Sensing Image Using Mask Image Modeling
Remote Sensing, 2023 Road extraction from a remote sensing image is a research hotspot due to its broad range of applications. Despite recent advancements, achieving precise road extraction remains challenging.
Zhenghong Li +3 more
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