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Leveraging multimodal data is an inherent requirement for comprehending geographic objects.
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W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , 2019, pp. 9621–9630
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A. Sadeghian, V. Kosaraju, A. Sadeghian, N. Hirose, H. Rezatofighi, and S. Savarese, “Sophie: An attentive gan for predicting paths compliant to social and physical constraints,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 1349–1358
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2019
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B. Li, Y. Guo, J. Yang, L. Wang, Y. Wang, and W. An, “Gated recurrent multiattention network for vhr remote sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–13, 2021
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P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 12, no. 7, pp. 2217–2226, 2019
2019
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M. A. Uy, Q.-H. Pham, B.-S. Hua, D. T. Nguyen, and S.-K. Yeung, “Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,” in International Conference on Computer Vision (ICCV) , 2019
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W. Zhang, P. Tang, and L. Zhao, “Remote sensing image scene classification using cnn-capsnet,” Remote Sensing , vol. 11, no. 5, p. 494, 2019
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2020
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