feat: implement comprehensive land cover classification pipeline with model benchmarking and experiment logging
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HYBRID FUSION ENSEMBLE: CNN embed + S1/S2 Rich features + XGB/LGBM/ETC
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Final Feature Vector: (443, 2694)
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Fold 1: 0.8876
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Fold 2: 0.9438
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Fold 3: 0.9438
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Fold 4: 0.9659
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Fold 5: 0.9432
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✅ Ensemble CV Mean: 0.9369 ± 0.0261
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📊 FINAL RESULTS V5 (ENSEMBLE + RADAR)
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✅ Hybrid Fusion Ensemble CV: 0.9369
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🏆 BEST: 0.9369
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