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Item: Title: Preprocessing Children: Item: Title: Height approximation Item: Title: Features Item: Title: Normalization and Imputation Item: Title: AI Model Item: Title: Training Process Children: Item: Title: Feature Selection Item: Title: Hyperparameter Tunning Item: Title: Full Model Training Item: Title: Partial Model Training Item: Title: Computational Optimizations Item: Title: Experimental studies and results Children: Item: Title: Error analysis Item: Title: Quality Analysis Item: Title: Conclusions Item: Title: CRediT authorship contribution statement Item: Title: Software availability Item: Title: Declaration of competing interest Item: Title: Acknowledgments Item: Title: Data availability Item: Title: References Info: Title: Optimizing canopy cover evaluation: A machine learning approach using LiDAR data Author: Pau Ventura Subject: Environmental Modelling and Software, 201 (2026) 106982. doi:10.1016/j.envsoft.2026.106982 Keywords: Machine learning,LiDAR data,Canopy cover 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