{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002604308,0.000263388,0.0002772705,0.0002140001,0.0004324787,0.0002677946,0.00006237761,0.0001512438,0.000005546665],"category_scores_gemma":[0.000119279,0.0003120414,0.00008172129,0.0003561906,0.0000422272,0.0004401989,0.00006052637,0.0004655652,0.000008240818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004741657,"about_ca_system_score_gemma":0.00003249733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001387231,"about_ca_topic_score_gemma":0.00001510676,"domain_scores_codex":[0.9985036,0.0001215358,0.0002854629,0.0004576731,0.0002364858,0.000395272],"domain_scores_gemma":[0.9991257,0.0002825425,0.0001157304,0.0002973949,0.00009951168,0.00007913203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001876844,0.00001179983,0.0003511724,0.0001214108,0.0001596427,0.00002889271,0.0006922213,0.09246105,0.7521254,0.00001269401,0.01185297,0.1419951],"study_design_scores_gemma":[0.0004582987,0.00002208027,0.008436622,0.00007477403,0.0000511982,0.00007018991,0.00007743699,0.9488618,0.004587098,0.0001837926,0.03683713,0.0003396263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6947752,0.0006738495,0.3016478,0.0004112896,0.001163409,0.0004517308,0.00004998314,0.0005569328,0.0002697463],"genre_scores_gemma":[0.8763926,0.0001861342,0.1214509,0.0001369092,0.0005622383,2.327267e-7,0.0003177844,0.000136147,0.000816996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8564007,"threshold_uncertainty_score":0.9999332,"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."}}