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Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping.
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C. Tian, C. Li, and J. Shi · 2018
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Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker · 2018
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L. Yan, B. Fan, H. Liu, C. Huo, S. Xiang, and C. Pan · 2019
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Y. Zou, Z. Yu, B. Kumar, and J. Wang · 2018
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Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings
Q. Chen, L. Wang, Y. Wu, G. Wu, Z. Guo, and S. L. Waslander · 2019
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W. Chen, Z. Jiang, Z. Wang, K. Cui, and X. Qian · 2019
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Standardgan: Multi-source domain adaptation for semantic segmentation of very high resolution satellite images by data standardization
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Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery
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LandCover.ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery
A. Boguszewski, D. Batorski, N. Ziemba-Jankowska, T. Dziedzic, and A. Zambrzycka · 2021
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RSNet: the search for remote sensing deep neural networks in recognition tasks
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