D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

By Lichen Zhou, Chuang Zhang, Ming Wu. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018

839 citations recorded by the source corpus.

Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with LinkNet architecture and has dilated convolution layers in its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. In the CVPR DeepGlobe 2018 Road Extraction Challenge, our best IoU scores on the validation set and the test set are 0.6466 and 0.6342 respectively.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/7447a957fe1a4922fb7e28cf672d3d84b2963d83

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In Computer Vision and Pattern Recognition

1. Wanli Ouyang
2. Lei Bai
3. Sijie Zhao
4. Hao Chen
5. Pengfeng Xiao
6. Xue-liang Zhang

Citations in the corpus

• RS-Mamba for Large Remote Sensing Image Dense Prediction (2024, methodology): “…models include FCN [40], SegNet [41], U-Net [19], PSPNet [42], HRNet [43], MA-FCN [44], Deeplabv3 + [45], ResUNet [46], MAP-Net [47], D-LinkNet [48], and SIINet [49], the compared transformer-based models include Segformer [50] and Road-Former [10], and the CNN-transformer hybrid models…”