Super-resolving and composing building dataset using a momentum spatial-channel attention residual feature aggregation network

Berman, 2018, The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks, Proc. CVPR, 4413–4421 Cai, 2021, A comparative study of deep learning approaches to rooftop detection in aerial images, Can. J. Remote Sens., 47, 413, 10.1080/07038992.2021.1915756 Cao, 2019, GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond, Proc. ICCVW, 1971 Chen, 2017, SCA-CNN: Spatial and channel-wise attention in convolutional networks for image captioning, Proc. CVPR, 5659–5667 Chen, Q., Wang, L., Wu, Y., Wu, G., Guo, Z., and Waslander, S.L. 2018a. Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings. arXiv 2018. arXiv preprint arXiv:1807.09532. http://dx.doi.org/10.1016/j.isprsjprs.2018.11.011. Chollet, 2017, Xception: Deep learning with depthwise separable convolutions, Proc. CVPR, 1251–1258 Dai, 2019, Second-order attention network for single image super-resolution, In Proc. CVPR, 11065–11074 Dong, 2015, Image super-resolution using deep convolutional networks, IEEE Trans. Patt. Anal. Mach. Intell., 38, 295, 10.1109/TPAMI.2015.2439281 Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J., 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Duveiller, 2010, A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing, Remote Sens. Environ., 114, 2637, 10.1016/j.rse.2010.06.001 Farrow, 2011, Nyquist-Shannon sampling theorem applied to refinements of the atomic pair distribution function, Phys. Rev. B, 84, 10.1103/PhysRevB.84.134105 GFDRR Labs, 2020. Open Cities AI Challenge Dataset, Version 1.0, Radiant MLHub. https://doi.org/10.34911/rdnt.f94cxb. He, 2021, Waterloo Building Dataset, V1, Harvard Dataverse He, 2019, Bag of tricks for image classification with convolutional neural networks, In Proc. CVPR, 558–567 Ji, 2018, Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set, IEEE Trans. Geosci. Remote Sens., 57, 574, 10.1109/TGRS.2018.2858817 Kim, 2016, Deeply-recursive convolutional network for image super-resolution, In Proc. CVPR, 1637–1645 Lai, 2017, Deep Laplacian pyramid networks for fast and accurate super-resolution, In Proc. CVPR, 624–632 Lambert, 2020, MSeg: A composite dataset for multi-domain semantic segmentation, In Proc. CVPR, 2879–2888 Ledig, 2017, Photo-realistic single image super-resolution using a generative adversarial network, In Proc. CVPR, 4681–4690 Lim, 2017, Enhanced deep residual networks for single image super-resolution, In Proc. CVPRW, 136–144 Liu, 2020, Residual feature aggregation network for image super-resolution, In Proc. CVPR, 2359–2368 Maggiori, 2017, Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark, In Proc. IGARSS, 3226–3229 Mao, 2016, Image restoration using convolutional auto-encoders with symmetric skip connections, In Proc. NeurIPS, 29, 2802 Marivani, 2020, Joint image super-resolution via recurrent convolutional neural networks with coupled sparse priors, In Proc. ICIP, 868–872 Mnih, 2013 Mohanty, 2020, Deep learning for understanding satellite imagery: An experimental survey, Front. Artif. Intell, 3, 10.3389/frai.2020.534696 Molchanov, 2019, Pruning convolutional neural networks for resource efficient inference Qin, 2018, Diagonalwise refactorization: An efficient training method for depthwise convolutions, In Proc. IJCNN, 1–8 Roscher, 2020, SemCity Toulouse: A benchmark for building instance segmentation in satellite images, ISPRS Annals, 5, 109 Sabour, 2017, December. Dynamic routing between capsules, In Proc., NeurIPS, 3859 Shi, 2016, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, In Proc. CVPR, 1874–1883 Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W. and Wang, J., 2019. High-resolution representations for labeling pixels and regions. arXiv preprint arXiv:1904.04514. Tai, 2017, Image super-resolution via deep recursive residual network, In Proc. CVPR, 3147–3155 Tong, 2017, Image super-resolution using dense skip connections, In Proc. CVPR, 4799–4807 Van Etten, A., Lindenbaum, D., and Bacastow, T.M. 2018. SpaceNet: A remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232. Woo, 2018, Cbam: Convolutional block attention module, In Proc. ECCV, 3–19 Yang, 2014, Single-image super-resolution: A benchmark, In Proc. ECCV, 372–386 Zhang, 2018, Image super-resolution using very deep residual channel attention networks, In Proc. ECCV, 286–301 Zhao, 2020, SCAU-Net: Spatial-channel attention U-Net for gland segmentation, Front. Bioeng. Biotech., 8, 670, 10.3389/fbioe.2020.00670