{"id":"W2119241338","doi":"10.1109/rsete.2011.5965626","title":"Urban land-cover classification based on swarm intelligence from high resolution remote sensing imagery","year":2011,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Land cover; Computer science; Remote sensing; Contextual image classification; Classifier (UML); Particle swarm optimization; Artificial intelligence; High resolution; Statistical classification; Pattern recognition (psychology); Land use; Geography; Machine learning; Image (mathematics); Engineering","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":[0.0004431904,0.0002718227,0.0004076961,0.0009468569,0.0002051905,0.0005508712,0.0002224452,0.000259293,0.0003536287],"category_scores_gemma":[0.001231192,0.0001813444,0.0003707135,0.0004675493,0.0002765906,0.0005453177,0.0001897435,0.0002772562,0.0001498658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002878705,"about_ca_system_score_gemma":0.0001575177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002971445,"about_ca_topic_score_gemma":0.002803289,"domain_scores_codex":[0.9998547,0.00003824099,0.0000094689,0.00002727758,0.00005636918,0.00001403738],"domain_scores_gemma":[0.9996046,0.0002095328,0.0000558399,0.0000318388,0.0000816374,0.00001649839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002108448,0.0002160318,0.02074908,0.0001657887,0.0001802799,0.0002031439,0.0002135158,0.5803555,0.03472921,0.002360881,0.001488127,0.3591276],"study_design_scores_gemma":[0.000005951393,0.00002670322,0.003180576,0.000003140608,0.000008651442,0.00001743263,0.00001649782,0.9944147,0.001711856,0.0004479893,0.0001627118,0.000003827608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4359764,0.0003286753,0.559767,0.0002607659,0.0000538903,0.0001120921,0.0001105006,0.000608471,0.002782167],"genre_scores_gemma":[0.8800269,0.0001358151,0.1187791,0.00002741424,0.00002481836,0.00004543297,0.0002093967,0.00002148662,0.0007296373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002971445,"threshold_uncertainty_score":0.00590831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0454386174173113,"score_gpt":0.226961048688635,"score_spread":0.1815224312713237,"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."}}