{"id":"W2791075437","doi":"10.5623/cig2017-401","title":"Building Extraction From Fused LiDAR and Hyperspectral Data using Random Forest Algorithm","year":2017,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Lidar; Hyperspectral imaging; Ranging; Remote sensing; Random forest; Extraction (chemistry); Computer science; Linear discriminant analysis; Algorithm; Fusion; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004133122,0.00123724,0.001244613,0.002513852,0.000527749,0.0007884988,0.001001378,0.0009134123,0.002275118],"category_scores_gemma":[0.0008146309,0.0006856896,0.00173014,0.001911368,0.0002830615,0.001046934,0.0007374061,0.0008742582,0.002706736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002543942,"about_ca_system_score_gemma":0.001030952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004267653,"about_ca_topic_score_gemma":0.0074474,"domain_scores_codex":[0.9995091,0.00003676598,0.00003003538,0.0001403655,0.0002056453,0.00007807193],"domain_scores_gemma":[0.9996475,0.00007767906,0.00003894864,0.00006257041,0.0001565307,0.00001669664],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001782453,0.0001744548,0.001848458,0.0002077617,0.0001342997,0.0001492936,0.00006293529,0.09167709,0.069151,0.002097254,0.004024668,0.8302945],"study_design_scores_gemma":[0.00001995957,0.00004555899,0.002243719,0.00002195308,0.00009717092,0.0001981825,0.00003321873,0.9628474,0.02910873,0.002315465,0.003037091,0.000031493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01683847,0.0001990608,0.9788989,0.00004226774,0.00003221119,0.00003907092,0.0002194053,0.002793758,0.0009367797],"genre_scores_gemma":[0.1933049,0.0002973524,0.801699,0.0000565205,0.00004448563,0.00009629289,0.002056812,0.0002689151,0.00217566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004267653,"threshold_uncertainty_score":0.008485615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05049766309727408,"score_gpt":0.2989953821201956,"score_spread":0.2484977190229215,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}