{"id":"W2069748987","doi":"10.1080/01431161003745590","title":"A method for obtaining and applying classification parameters in object-based urban rooftop extraction from VHR multispectral images","year":2011,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Extraction (chemistry); Remote sensing; Computer science; Object (grammar); Multispectral pattern recognition; Object based; Artificial intelligence; Pattern recognition (psychology); Computer vision; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005469921,0.000719121,0.0006135649,0.001622233,0.0005640754,0.001141418,0.0008139322,0.0007227537,0.001768886],"category_scores_gemma":[0.001283047,0.0004781696,0.0005077227,0.001294504,0.0004045619,0.000761938,0.0005935019,0.000695896,0.001811648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003330577,"about_ca_system_score_gemma":0.0007192252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004580839,"about_ca_topic_score_gemma":0.00592325,"domain_scores_codex":[0.9996487,0.00003858063,0.0000274142,0.0001022063,0.0001473384,0.00003578307],"domain_scores_gemma":[0.9995264,0.000110023,0.00005352211,0.00007846074,0.0002129083,0.00001875083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001374634,0.0001427569,0.003429786,0.0001355119,0.00006393126,0.00008274108,0.0001291808,0.01770052,0.1349102,0.001517756,0.00195353,0.8397966],"study_design_scores_gemma":[0.00005865453,0.0001466519,0.02077983,0.00005648465,0.000153642,0.0005503046,0.0002831452,0.7517077,0.2088716,0.00286181,0.01439427,0.0001359031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01657061,0.0001501538,0.980186,0.00002792853,0.00002923258,0.0001235253,0.0001607763,0.001870735,0.0008811381],"genre_scores_gemma":[0.1106159,0.0002121406,0.8869072,0.00002955405,0.00002379772,0.0001912158,0.0003711537,0.0001590371,0.001490098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004580839,"threshold_uncertainty_score":0.009108305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04699599688226821,"score_gpt":0.3061145417061512,"score_spread":0.259118544823883,"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."}}