CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion network for LULC mapping under cloud contamination that achieves 86.60% OA, 83.29% F1, and 73.51% mIoU on a new global benchmark of 40,223 samples.
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A homogeneous multi-view CNN ensemble for ordinal defoliation classification on oak trees outperforms single-view and pairwise baselines.
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Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion network for LULC mapping under cloud contamination that achieves 86.60% OA, 83.29% F1, and 73.51% mIoU on a new global benchmark of 40,223 samples.
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A novel ordinal multi-view aggregation scheme for oak defoliation
A homogeneous multi-view CNN ensemble for ordinal defoliation classification on oak trees outperforms single-view and pairwise baselines.