Results 21 to 30 of about 185,046 (290)
Multiscale Road Extraction in Remote Sensing Images. [PDF]
Comput Intell Neurosci, 2019 Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the ...
Wulamu A, Shi Z, Zhang D, He Z.
europepmc +4 more sources
DeepWindow: Sliding Window Based on Deep Learning for Road Extraction From Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 The road centerline extraction is the key step of the road network extraction and modeling. The hand-craft feature engineering in the traditional road extraction methods is unstable, which makes the extracted road centerline deviated from the road center
Renbao Lian, Liqin Huang
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Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation, and road network update, etc. Convolutional neural network (CNN)-based methods have presented many achievements for road extraction ...
Ziyi Chen, Cheng Wang, Jonathan Li
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Road Extraction by Deep Residual U-Net [PDF]
IEEE Geoscience and Remote Sensing Letters, 2018 Submitted to IEEE Geoscience and Remote Sensing ...
Zhengxin Zhang +2 more
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Remote SensingThe identification of roads from satellite imagery plays an important role in urban design, geographic referencing, vehicle navigation, geospatial data integration, and intelligent transportation systems. The use of deep learning methods has demonstrated
Bo Zhong +6 more
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JointNet: A Common Neural Network for Road and Building Extraction
Remote Sensing, 2019 Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method.
Zhengxin Zhang +2 more
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Road Extraction from High-Resolution Remote Sensing Images Based on EDRNet Model [PDF]
Jisuanji gongcheng, 2021 The existing methods for extracting the road parts from high-resolution remote sensing images are limited by the incomplete extraction results and poor boundary quality.To address the problem, a new method based on the EDRNet model is proposed for ...
HE Xiaohui, LI Daidong, LI Panle, HU Shaokai, CHEN Mingyang, TIAN Zhihui, ZHOU Guangsheng
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Review on Active and Passive Remote Sensing Techniques for Road Extraction
Remote Sensing, 2021 Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images ...
Jianxin Jia +12 more
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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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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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