References
[1]. Mackaness, W.; Edwards, G. July 2002. The importance of modelling pattern and structure in automated map generalisation. In Proceedings of the Joint ISPRS/ICA Workshop on Multi-Scale Representations of Spatial Data, Ottawa, ON, Canada, 7–8.
[2]. Amap: http://ditu.amap.com/
[3]. Google Maps: http://maps.google.com/
[4]. Scheider, S., & Possin, J. (2012). Affordance-based individuation of junctions in Open Street Map. Journal of Spatial Information Science, (4), 31-56.
[5]. Xu, Z., Meng, Y., Li, Z., & Li, M. (2011). Recognition of structures of typical road junctions based on directed attributed relational graph. Acta Geodaetica et Cartographica Sinica, 40(1), 125-131.
[6]. Li, H., Hu, M., & Huang, Y. (2019). Automatic identification of overpass structures: a method of deep learning. ISPRS International Journal of Geo-Information, 8(9), 421.
[7]. Haiwei, H. E., Haizhong, Q. I. A. N., Limin, X. I. E., & Peixiang, D. U. A. N. (2018). Interchange recognition method based on CNN. Acta Geodaetica et Cartographica Sinica, 47(3), 385.
[8]. Huang, L., Yang, D., Lang, B., & Deng, J. (2018). Decorrelated batch normalization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 791-800).
[9]. Zhang, F., Xu, X., & Qiao, Y. (2015, December). Deep classification of vehicle makers and models: The effectiveness of pre-training and data enhancement. In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 231-236). IEEE.
[10]. Namozov, A., & Im Cho, Y. (2018, September). An improvement for medical image analysis using data enhancement techniques in deep learning. In 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT) (pp. 1-3). IEEE.
[11]. Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3642-3649). IEEE.
[12]. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[13]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
[14]. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2015). Deep Residual Learning for Image Recognition. arXiv 2015, arXiv:1512.03385.
Cite this article
Xie,Z. (2023). Classification of overpass structures based on convolutional neural networks. Applied and Computational Engineering,6,1166-1175.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Mackaness, W.; Edwards, G. July 2002. The importance of modelling pattern and structure in automated map generalisation. In Proceedings of the Joint ISPRS/ICA Workshop on Multi-Scale Representations of Spatial Data, Ottawa, ON, Canada, 7–8.
[2]. Amap: http://ditu.amap.com/
[3]. Google Maps: http://maps.google.com/
[4]. Scheider, S., & Possin, J. (2012). Affordance-based individuation of junctions in Open Street Map. Journal of Spatial Information Science, (4), 31-56.
[5]. Xu, Z., Meng, Y., Li, Z., & Li, M. (2011). Recognition of structures of typical road junctions based on directed attributed relational graph. Acta Geodaetica et Cartographica Sinica, 40(1), 125-131.
[6]. Li, H., Hu, M., & Huang, Y. (2019). Automatic identification of overpass structures: a method of deep learning. ISPRS International Journal of Geo-Information, 8(9), 421.
[7]. Haiwei, H. E., Haizhong, Q. I. A. N., Limin, X. I. E., & Peixiang, D. U. A. N. (2018). Interchange recognition method based on CNN. Acta Geodaetica et Cartographica Sinica, 47(3), 385.
[8]. Huang, L., Yang, D., Lang, B., & Deng, J. (2018). Decorrelated batch normalization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 791-800).
[9]. Zhang, F., Xu, X., & Qiao, Y. (2015, December). Deep classification of vehicle makers and models: The effectiveness of pre-training and data enhancement. In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 231-236). IEEE.
[10]. Namozov, A., & Im Cho, Y. (2018, September). An improvement for medical image analysis using data enhancement techniques in deep learning. In 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT) (pp. 1-3). IEEE.
[11]. Ciregan, D., Meier, U., & Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3642-3649). IEEE.
[12]. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[13]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
[14]. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2015). Deep Residual Learning for Image Recognition. arXiv 2015, arXiv:1512.03385.