Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network
By Guangliang Cheng, Ying Wang, Shibiao Xu, Hongzhen Wang, Shiming Xiang, Chunhong Pan. IEEE Transactions on Geoscience and Remote Sensing, 2017
386 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/d146d7726369be92c6d67ff201a8a9300540a03f
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Yunhong Wang
- Yanfei Zhong
- Ce Zhang
- Ritwik Gupta
- Favyen Bastani
- Piper Wolters
- Joe Ferdinando
- Zhuo Zheng
In Environmental Engineering
- Ce Zhang
- Yunhong Wang
- Peter M. Atkinson
- Zhengxin Zhang
- Chen Wu
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- Qingjie Liu
- François Waldner
In Ocean Engineering
- Hari Balakrishnan
- Yunhong Wang
- Zhengxin Zhang
- Chen Wu
- Peter A. Caccetta
- Qingjie Liu
- François Waldner
- Yanfei Zhong
Citations in the corpus
- RoadTracer: Automatic Extraction of Road Networks from Aerial Images (2018, methodology): “Almost universally, they begin by segmenting the image, classifying each pixel in the input as either road or non-road [5, 10].”
- Satlas: A Large-Scale, Multi-Task Dataset for Remote Sensing Image Understanding (2022, methodology): “ized polyline extraction architectures have been shown to improve accuracy [9, 30, 49], we opt to employ the simpler segmentation approach [56] where we apply the UNet head to segment images for polyline categories, and postprocess the segmentation probabilities with binary thresholding, morphologic”
- Road Extraction by Deep Residual U-Net (2017, methodology): “There are also methods that extract both road areas and centerline, simultaneously [9].”
- ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data (2019, background): “, building, road, or certain vegetation types [30, 11, 58], or multiple classes all together [44, 28, 33, 37].”
- Joint Deep Learning for land cover and land use classification (2019, background): “…the CNN has demonstrated great potential in many remotely sensed tasks such as vehicle detection (Chen et al., 2014; Dong et al., 2015), road network extraction (Cheng et al., 2017), remotely sensed scene classification (Othman et al., 2016), and semantic segmentation (Zhao et al., 2017).”
- Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery (2020, methodology): “Semantic Segmentation in Remote Sensing Community There are a lot of applications using semantic segmentation technique in the remote sensing community, such as land use and land cover (LULC) classification [46, 23, 47], building extraction [45, 24, 43, 15], road extraction [28, 9, 2, 34, 3], vehicl”