{"id":"W3080919729","doi":"10.5194/isprs-archives-xliii-b3-2020-113-2020","title":"AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY","year":2020,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Correctness; Intersection (aeronautics); Completeness (order theory); Computer science; Feature extraction; Artificial intelligence; Computer vision; Image (mathematics); Segmentation; Image segmentation; Pattern recognition (psychology); Geography; Mathematics; Cartography; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002915045,0.0005993199,0.0003643109,0.001955358,0.0003370442,0.0006222457,0.000536362,0.0004367915,0.002106708],"category_scores_gemma":[0.0006369891,0.0002764637,0.0004038248,0.00116984,0.0002960248,0.0005200439,0.0003669009,0.0003024135,0.001569784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004072191,"about_ca_system_score_gemma":0.0006982044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008381286,"about_ca_topic_score_gemma":0.01951322,"domain_scores_codex":[0.999522,0.0000542736,0.00002097426,0.0001064246,0.0002258867,0.00007045967],"domain_scores_gemma":[0.9996693,0.00004808153,0.00004220361,0.00006098303,0.0001638076,0.00001553754],"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":[0.0003033041,0.0001022798,0.01846216,0.0003838643,0.0000963588,0.0003071559,0.0002346633,0.01123544,0.3870553,0.001394131,0.004884219,0.5755413],"study_design_scores_gemma":[0.00004316808,0.0001875528,0.1131872,0.00005644114,0.0001519747,0.001027274,0.0004466785,0.3761606,0.4937691,0.001074872,0.0138191,0.00007599629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3536854,0.0006289483,0.629339,0.0001290172,0.00005412311,0.0002818511,0.001619345,0.005467518,0.008794697],"genre_scores_gemma":[0.6418211,0.0003469693,0.3506131,0.00003445016,0.00001451368,0.00009660672,0.003136285,0.0001441968,0.003792877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008381286,"threshold_uncertainty_score":0.01666498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017465544981908,"score_gpt":0.2507267277201726,"score_spread":0.2305520722703535,"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."}}