{"id":"W2728080634","doi":"10.29252/jgit.5.1.89","title":"Evaluation of SLIC superpixel and DBSCAN clustering algorithms in segmentation of ultra-high resolution remote sensing imageryover urban areas","year":2017,"lang":"en","type":"article","venue":"Journal of Geospatial Information Technology","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"DBSCAN; Cluster analysis; Computer science; Segmentation; Remote sensing; Artificial intelligence; High resolution; Pattern recognition (psychology); Resolution (logic); Computer vision; Geography; Fuzzy clustering; Canopy clustering algorithm","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":[0.002915028,0.001403311,0.001179554,0.003288643,0.0008760105,0.00176933,0.001492485,0.001285607,0.001868512],"category_scores_gemma":[0.004981934,0.0003275106,0.0007664498,0.002625562,0.0005858964,0.001231917,0.0007779807,0.000432115,0.0003780933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001468942,"about_ca_system_score_gemma":0.00127579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03148618,"about_ca_topic_score_gemma":0.02909134,"domain_scores_codex":[0.9981018,0.0005166247,0.0001444052,0.0002999162,0.0007556621,0.0001815569],"domain_scores_gemma":[0.9968193,0.001240906,0.000191491,0.0002545467,0.00130206,0.0001918069],"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":[0.005187642,0.001031436,0.01835947,0.0009244299,0.000686752,0.0002337271,0.0004792185,0.4862291,0.02541888,0.003194795,0.004635092,0.4536195],"study_design_scores_gemma":[0.00003761657,0.0003607901,0.006671057,0.00001515197,0.00007510148,0.00006182842,0.0002186075,0.9813408,0.01003711,0.0005272313,0.0006300923,0.000024671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8700142,0.003015602,0.1195286,0.0002687643,0.0002003812,0.0002800566,0.0006411989,0.001952351,0.004098985],"genre_scores_gemma":[0.9062247,0.0007247311,0.0893614,0.00007026328,0.00003567374,0.00007112875,0.001789384,0.0002069827,0.001515744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03148618,"threshold_uncertainty_score":0.0626058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673742150268415,"score_gpt":0.2595445872286255,"score_spread":0.2428071657259414,"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."}}