{"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 œinternational 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006610263,0.00031138,0.0003509685,0.0006045521,0.0007383744,0.0004010331,0.0005982075,0.00009658493,0.000007229611],"category_scores_gemma":[0.0005905177,0.0002175502,0.0002704524,0.0006205188,0.001241092,0.0006315401,0.0002752349,0.0003420765,0.000001685357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000397488,"about_ca_system_score_gemma":0.00007861819,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5600609,"about_ca_topic_score_gemma":0.06643441,"domain_scores_codex":[0.9972109,0.000143324,0.001179891,0.0002873024,0.0008756805,0.0003028547],"domain_scores_gemma":[0.9976234,0.0007015787,0.00111214,0.0002274345,0.0002204537,0.0001149872],"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":[0.0001144824,0.00001136906,0.00004823291,0.00007805436,0.00009834461,9.826466e-8,0.002915082,0.003868383,0.02969811,7.294712e-7,0.00002498742,0.9631422],"study_design_scores_gemma":[0.000598324,0.0001353664,0.004207258,0.0003157729,0.00006860391,0.00006476251,0.001971185,0.9504988,0.03874144,0.002396927,0.0007757204,0.0002258466],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063701,0.00003358214,0.8884015,0.001291874,0.001634886,0.000560389,0.0001547673,0.00009440562,0.001458496],"genre_scores_gemma":[0.9955784,0.0001820177,0.00366659,0.0003376832,0.0001609252,7.514445e-7,0.00005001546,0.00001291538,0.00001071082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9629163,"threshold_uncertainty_score":0.9506007,"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."}}