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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Formal abductive explanations are applied to AI diagnostic models to produce minimal sufficient symptom sets that align with clinical reasoning while preserving predictive 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.
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Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms
Formal abductive explanations are applied to AI diagnostic models to produce minimal sufficient symptom sets that align with clinical reasoning while preserving predictive accuracy.