Open Access
| Issue |
E3S Web Conf.
Volume 351, 2022
10th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|
|
|---|---|---|
| Article Number | 01043 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/e3sconf/202235101043 | |
| Published online | 24 May 2022 | |
- Reichstein, M., Camps-Valls, G., Stevens, B. et al. Deep learning and process understanding for data-driven Earth system science. Nature vol. 566, pp. 195–204, 2019. https://doi.org/10.1038/s41586-019-0912-1 [CrossRef] [PubMed] [Google Scholar]
- Lei Ma, Yu Liuc, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, Brian Alan Johnson. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing 152 (2019) 166–177 https://doi.org/10.1016Zj.isprsjprs.2019.04.015 [CrossRef] [Google Scholar]
- Y. Altun et al. (Eds.): Urban Water Flow and Water Level Prediction Based on Deep Learning, ECML PKDD 2017, Part III, LNAI 10536, pp. 317–329, 2017. https://doi.org/10.1007/978-3-319-71273-4_26 [Google Scholar]
- Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y., 2014. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (6), 2094–2107. [CrossRef] [Google Scholar]
- Zou, Q., Ni, L., Zhang, T., Wang, Q., 2015. Deep learning based feature selection for remote sensing scene classification. IEEE Geosci. Remote Sens. Lett. 12 (11), 2321–2325. [CrossRef] [Google Scholar]
- Romero, A., Gatta, C., Camps-Valls, G., 2016. Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 54 (3),1349–1362. [CrossRef] [Google Scholar]
- https://www.flickr.com/services/api, 2021-07 [Google Scholar]
- Brown, D.G., Walker, R., Manson, S., Seto, K. (2012). Modeling Land Use and Land Cover Change. In:, et al. Land Change Science. Remote Sensing and Digital Image Processing, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-2562-4_23 [Google Scholar]
- National Academies of Sciences, Engineering, and Medicine. 2001. Grand Challenges in Environmental Sciences. Washington, DC: The National Academies Press. https://doi.org/10.17226/9975. [Google Scholar]
- Tan C., Sun F., Kong T., Zhang W., Yang C., Liu C. (2018) A Survey on Deep Transfer Learning. In: Kurkova, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning - ICANN 2018. ICANN 2018. Lecture Notes in Computer Science, vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-W27 [Google Scholar]
- He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778. 2016. [Google Scholar]
- Yi Yang and Shawn Newsam. Bag-Of-Visual-Words and Spatial Extensions for Land-Use Classification, ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2010. [Google Scholar]
- https://docs.fast.ai/, 2021/07 [Google Scholar]
- https://pytorch.org/, 2021/07 [Google Scholar]
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