{"id":"W4224988789","doi":"10.3390/rs14092097","title":"Optimum Feature and Classifier Selection for Accurate Urban Land Use/Cover Mapping from Very High Resolution Satellite Imagery","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Support vector machine; Feature selection; Classifier (UML); Random forest; Particle swarm optimization; Land cover; Cascading classifiers; Data mining; Machine learning; Random subspace method; Land use","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0009312934,0.0007001504,0.0007337857,0.001408447,0.0003675414,0.0006292963,0.000400537,0.0004864654,0.0006934531],"category_scores_gemma":[0.001775775,0.0002579678,0.0007480326,0.0007161262,0.0002278548,0.0007093759,0.0002603601,0.000394875,0.0004243839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002878678,"about_ca_system_score_gemma":0.0006170521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547678,"about_ca_topic_score_gemma":0.001753017,"domain_scores_codex":[0.9994184,0.0001259168,0.00004266417,0.0001246173,0.0002209683,0.00006740215],"domain_scores_gemma":[0.9995592,0.0001521814,0.00005606467,0.00004058142,0.0001760122,0.00001590101],"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":[0.000340329,0.0003166679,0.01144303,0.0002015307,0.0001399524,0.0003293384,0.0001654513,0.1278649,0.1344941,0.002282276,0.002723364,0.7196992],"study_design_scores_gemma":[0.00002438031,0.0002305128,0.01402134,0.00002530084,0.0001008317,0.0001964383,0.000116469,0.9327819,0.04813135,0.001822305,0.002510394,0.0000387996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1661184,0.0003849989,0.8313357,0.0001081806,0.00002204587,0.000102504,0.0001283548,0.0006690747,0.001130779],"genre_scores_gemma":[0.6145917,0.0002151957,0.3834038,0.00004089832,0.00002995992,0.0001566562,0.0005370099,0.0000755018,0.000949294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001547678,"threshold_uncertainty_score":0.004925191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02346626000629997,"score_gpt":0.2191316873339582,"score_spread":0.1956654273276582,"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."}}