COLD-CI provides 123,736 vector polygons labeling cocoa (1,788 km²) and background (4,208 km²) areas in Cote d'Ivoire with 99% independent validation accuracy.
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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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COLD-CI: A large-scale very high-resolution label polygon dataset for cocoa and non-cocoa classification in Cote d'Ivoire
COLD-CI provides 123,736 vector polygons labeling cocoa (1,788 km²) and background (4,208 km²) areas in Cote d'Ivoire with 99% independent validation accuracy.
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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.