## 4 Discussion and Future Work
In Proceedings of the 2018 41 st International Conference on Telecommunications and Signal Processing (TSP), Athens, Greece, 4-6 July 2018; pp. 1-5. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. _IEEE Trans. Geosci. Remote Sens._**2017**, _55_, 3322-3337. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] [PERSON] Building and road detection from large aerial imagery. In _Image Processing: Machine Vision Applications VIII_; International Society for Optics and Photonics: Bellingham, WA, USA, 2015; Volume 9405, p. 94050K. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON] Road extraction by deep residual u-net. _IEEE Geosci. Remote Sens. Lett._**2018**, _15_, 749-753. [CrossRef]* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Roads detection of aerial image with FCN-CRF model. In Proceedings of the 2018 IEEE Visual Communications and Image Processing (VCIP), Taichung, Taiwan, 9-12 December 2018. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Road structure refined CNN for road extraction in aerial image. _IEEE Geosci. Remote Sens. Lett._**2017**, _14_, 709-713. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Fully convolutional networks for building and road extraction: Preliminary results. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10-15 July 2016; pp. 1591-1594. * [PERSON] et al. (2019) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Evaluation of Transfer Learning Techniques with Convolutional Neural Networks (CNNs) to Detect the Existence of Roads in High-Resolution Aerial Imagery. In _Applied Informatics_; [PERSON], [PERSON], [PERSON] [PERSON], [PERSON], Eds. ; Springer International Publishing: Cham, Switzerland, 2019; pp. 185-198. * [PERSON] et al. (2019) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] A Deep Convolutional Neural Network to Detect the Existence of Geospatial Elements in High-Resolution Aerial Imagery. _Proceedings_**2019**, _19_, 17. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages. _Remote Sens._**2020**, _12_, 765. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. _IEEE Trans. Geosci. Remote Sens._**2017**, _55_, 881-893. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June 2016; pp. 2818-2826. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] Neural Architecture Search with Reinforcement Learning. _arXiv_**2016**, arXiv:1611.01578. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Learning Transferable Architectures for Scalable Image Recognition. _arXiv_**2017**, arXiv:1707.07012. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Efficient Neural Architecture Search via Parameter Sharing. _arXiv_**2018**, arXiv:1802.03268. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Hierarchical Representations for Efficient Architecture Search. _arXiv_**2017**, arXiv:1711.00436. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Regularized Evolution for Image Classifier Architecture Search. _arXiv_**2018**, arXiv:1802.01548. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Evolutionary Neural AutoML for Deep Learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2019), Prague, Czech Republic, 13-17 July 2019. * [PERSON] et al.