Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery
By Alexander V. Buslaev, Selim S. Seferbekov, Vladimir I. Iglovikov, Alexey A. Shvets. 2018
164 citations recorded by the source corpus.
Analysis of high-resolution satellite images has been an important research topic for traffic management, city planning, and road monitoring. One of the problems here is automatic and precise road extraction. From an original image, it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. In this paper, we propose an approach for automatic road extraction based on a fully convolutional neural network of U-net family. This network consists of ResNet-34 pre-trained on ImageNet and decoder adapted from vanilla U-Net. Based on validation results, leaderboard and our own experience this network shows superior results for the DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network uses moderate memory that allows using just one GTX 1080 or 1080ti video cards to perform whole training and makes pretty fast predictions.
Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Ocean Engineering
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Alexandr A Kalinin
- Vladimir I. Iglovikov
- Alex Parinov
- Eugene Khvedchenya
- Alexander V. Buslaev
- Artur Kuzin
- Ruslan Dautov
- Artur Fattakhov
In Ocean Engineering
Citations in the corpus
- Albumentations: fast and flexible image augmentations (2018, methodology): “ a random transformation from dihedral group Dih 4 [11]. Highperforming solutions to other challenges also implemented similar types of image augmentations, for example, for automatic road extraction [12] and for multi-class land segmentation [13] from satellite imagery. Fig. 3 provides examples of ”
- Camera Model Identification Using Convolutional Neural Networks (2018, background): “Problems in satellite [1], [2], [3], [4], medical [5], [6], [7], [8] or any other imagery are successfully tackled with these techniques, routinely beating human performance in many tasks, including classification, segmentation, and detection.”
- Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends (2020, background): “The U-Net design was modified, for instance in Tiramisu [68], which combines U-Net structure and DenseNet [46] building blocks, and in U-Net++ [125], where nested dense building blocks perform convolution on the skip connections.”