Abstract
Infrastructure detection and monitoring traditionally required manual identification of geospatial objects in aerial imagery but advances in deep learning and computer vision enabled the researchers in the field of remote sensing to successfully apply transfer learning from pretrained models on large-scale datasets for the task of geospatial object detection. However, they mostly focused on objects with clearly defined boundaries that are independent of the background (e.g. airports, airplanes, buildings, ships, etc.). What happens when we have to deal with more complicated, continuous objects like roads? In this paper we will review four of the best-known CNN architectures (VGGNet, Inception-V3, Xception, Inception-ResNet) and apply feature extraction and fine-tuning techniques to detect the existence of roads in aerial orthoimages divided in tiles of 256 × 256 pixels in size. We will evaluate each model´s performance on unseen test data using the accuracy metric and compare the results with those obtained by a CNN especially built for this purpose.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates Inc, Red Hook (2012)
Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, Ar**v14091556 Cs, (September 2014)
Szegedy, C., et al.: Going Deeper with Convolutions, Ar**v14094842 Cs, September (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition, Ar**v151203385 Cs, December (2015)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Ar**v160207261 Cs, February (2016)
Pritt, M., Chern, G.: Satellite image classification with deep learning, In: 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–7. Washington, DC, USA (2017)
Zhou, W., Newsam, S., Li, C., Shao, Z.: PatternNet: a benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS J. Photogramm. Remote Sens. 145, 197–209 (2018)
Albert, A., Kaur, J., Gonzalez, M.C.: Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2017, pp. 1357–1366. Halifax, NS, Canada, 2017
Chollet, F.: Deep Learning with Python. Manning Publications Co, Shelter Island (2018)
Cai, B., Jiang, Z., Zhang, H., Zhao, D., Yao, Y.: Airport detection using end-to-end convolutional neural network with hard example mining. Remote Sens. 9(11), 1198 (2017)
Yang, H.L., Yuan, J., Lunga, D., Laverdiere, M., Rose, A., Bhaduri, B.: Building extraction at scale using convolutional neural network: map** of the United States. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(8), 2600–2614 (2018)
Li, Y., Zhang, Y., Huang, X., Yuille, A.L.: Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images. ISPRS J. Photogramm. Remote Sens. 146, 182–196 (2018)
Hutchison, D., et al.: Learning to detect roads in high-resolution aerial images. ECCV 2010. LNCS, vol. 6316, pp. 210–223. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15567-3_16
Zhang, Z., Liu, Q., Wang, Y.: Road Extraction by Deep Residual U-Net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)
Wang, Q., Gao, J., Yuan, Y.: Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection. IEEE Trans. Intell. Transp. Syst. 19(1), 230–241 (2018)
Alshehhi, R., Marpu, P.R., Woon, W.L., Mura, M.D.: Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J. Photogramm. Remote Sens. 130, 139–149 (2017)
Henry, C., Azimi, S.M., Merkle, N.: Road segmentation in SAR satellite images with deep fully-convolutional neural networks. IEEE Geosci. Remote Sens. Lett. 15(12), 1867–1871 (2018)
Liu, Y., Yao, J., Lu, X., **a, M., Wang, X., Liu, Y.: RoadNet: learning to comprehensively analyze road networks in complex urban scenes from high-resolution remotely sensed images. IEEE Trans. Geosci. Remote Sens. 57(4), 2043–2056 (2019)
Luque, B., Morros, J.R., Ruiz-Hidalgo, J.: Spatio-temporal road detection from aerial imagery using CNNs, In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications,, pp. 493–500. Porto, Portugal (2017)
Woźniak, M., Damaševičius, R., Maskeliūnas, R., Malūkas, U.: Real time path finding for assisted living using deep learning. JUCS - J. Univers. Comput. Sci. 24(4), 475–487 (2018)
Xu, Y., Goodacre, R.: On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J. Anal. Test. 2(3), 249–262 (2018)
May, R.J., Maier, H.R., Dandy, G.C.: Data splitting for artificial neural networks using SOM-based stratified sampling. Neural Netw. 23(2), 283–294 (2010)
Cira, C.I., Alcarria, R., Manso-Callejo, M.A., Serradilla, F.: A deep convolutional neural network to detect the existence of geospatial elements in high-resolution aerial imagery. Proceedings, 19(1), 17 (2019)
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization, Ar**v14126980 Cs, (December 2014)
Chen, X., Liu, S., Sun, R., Hong, M.: On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization, Ar**v180802941 Cs Math Stat, (August 2018)
Chollet, F., Xception: Deep Learning with Depthwise Separable Convolutions, Ar**v161002357 Cs, (October 2016)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks?, Ar**v14111792 Cs, (November 2014)
Acknowledgments
This research received funding from the Cartobot project, in collaboration with Instituto Geográfico Nacional (IGN), Spain. We thank all Cartobot participants for their help in generating the dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cira, CI., Alcarria, R., Manso-Callejo, MÁ., Serradilla, F. (2019). Evaluation of Transfer Learning Techniques with Convolutional Neural Networks (CNNs) to Detect the Existence of Roads in High-Resolution Aerial Imagery. In: Florez, H., Leon, M., Diaz-Nafria, J., Belli, S. (eds) Applied Informatics. ICAI 2019. Communications in Computer and Information Science, vol 1051. Springer, Cham. https://doi.org/10.1007/978-3-030-32475-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-32475-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32474-2
Online ISBN: 978-3-030-32475-9
eBook Packages: Computer ScienceComputer Science (R0)