{"id":"W4248618977","doi":"10.5194/isprsannals-iii-4-3-2016","title":"TRUE ORTHO GENERATION OF URBAN AREA USING HIGH RESOLUTION AERIAL PHOTOS","year":2016,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada)","funders":"","keywords":"Visibility; Pixel; Computer science; Compensation (psychology); Computer vision; Artificial intelligence; Perspective (graphical); Frame (networking); Measure (data warehouse); Remote sensing; Geography; Data mining; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.000825022,0.0001275987,0.0001968339,0.0002569981,0.0003895953,0.0001522904,0.0003499719,0.00005203226,0.000003393628],"category_scores_gemma":[0.0003703914,0.00007563442,0.00008487072,0.000703919,0.0003700987,0.001053513,0.0001716929,0.00005893828,8.978414e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001491056,"about_ca_system_score_gemma":0.00008870644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02189479,"about_ca_topic_score_gemma":0.0006015149,"domain_scores_codex":[0.9984204,0.0001030807,0.0005336771,0.0001886101,0.0005130551,0.0002411857],"domain_scores_gemma":[0.9985999,0.00008064605,0.0006031839,0.0003212317,0.0003278647,0.00006713477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001007826,0.00000564827,0.00005371587,0.000007346131,0.000004534508,1.006756e-7,0.0003485101,0.0005305344,0.06124772,0.0000826519,0.0001144398,0.9375947],"study_design_scores_gemma":[0.0001916234,0.00006826175,0.0002938368,0.0001018122,0.000003365996,0.00000795587,0.00005243129,0.7741146,0.2235973,0.0006712856,0.0008056051,0.0000919398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1647714,0.00002913936,0.8335167,0.0008641735,0.0004849122,0.0001397468,0.000006750096,0.00002404211,0.000163184],"genre_scores_gemma":[0.9701145,0.00005111315,0.02943178,0.0003321733,0.00005530278,7.900387e-8,0.000001144959,0.000002885453,0.00001103035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9375028,"threshold_uncertainty_score":0.9846185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08429295025879549,"score_gpt":0.3140538597233714,"score_spread":0.2297609094645759,"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."}}