{"id":"W2026496017","doi":"10.3808/jei.200300012","title":"Automated Road Extraction from Satellite Imagery Using Hybrid Genetic Algorithms and Cluster Analysis","year":2003,"lang":"en","type":"article","venue":"Journal of Environmental Informatics","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Artificial intelligence; Segmentation; Satellite imagery; Data mining; Genetic algorithm; Cluster (spacecraft); Satellite; Pattern recognition (psychology); Computer vision; Machine learning; Geography; Remote sensing; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000637934,0.0008406714,0.0008334352,0.001848734,0.0005151547,0.0008067279,0.00100872,0.0008472303,0.0007601826],"category_scores_gemma":[0.00167803,0.0003961418,0.0008189509,0.001301482,0.0005325183,0.0008616286,0.0005661875,0.0005346658,0.0003380795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006784016,"about_ca_system_score_gemma":0.0006388315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008093994,"about_ca_topic_score_gemma":0.009847968,"domain_scores_codex":[0.9995066,0.0001208448,0.00002235272,0.0001304056,0.0001778616,0.000041904],"domain_scores_gemma":[0.9993918,0.0002772223,0.0000806875,0.00008276197,0.0001489842,0.00001850968],"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":[0.0000942661,0.0001085028,0.002570404,0.0000950726,0.0001715917,0.0001087381,0.0001792124,0.5682511,0.02074458,0.004818679,0.0007191779,0.4021388],"study_design_scores_gemma":[0.00001397643,0.00003415937,0.001366544,0.000008410654,0.00002378369,0.00004779163,0.00003708552,0.9880396,0.005752997,0.003890748,0.000768066,0.00001686951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03392534,0.00008061962,0.9641023,0.00005537894,0.000009865807,0.00005062165,0.00003385889,0.0008082308,0.0009337236],"genre_scores_gemma":[0.1894545,0.00009222454,0.808783,0.00004462844,0.00001671735,0.00009159065,0.0001534057,0.0001382177,0.001225727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008093994,"threshold_uncertainty_score":0.01609373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007370731714605813,"score_gpt":0.2208618356416281,"score_spread":0.2134911039270223,"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."}}