{"id":"W2948890734","doi":"10.5194/isprs-archives-xlii-2-w13-1185-2019","title":"AUTOMATED VISIBILITY FIELD EVALUATION OF TRAFFIC SIGN BASED ON 3D LIDAR POINT CLOUDS","year":2019,"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":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Visibility; Traffic sign; Computer science; Computer vision; Point cloud; Artificial intelligence; Sign (mathematics); Field (mathematics); Lidar; Laser scanning; Point (geometry); Road surface; Remote sensing; Laser; Geography; Engineering; Optics; Mathematics","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":[0.003309944,0.0003809541,0.0004274132,0.001025868,0.0008215779,0.000629616,0.001969408,0.0001068133,0.00001668213],"category_scores_gemma":[0.0009979246,0.0002451876,0.0004124552,0.001269116,0.001357434,0.0006490616,0.0006770259,0.0004016464,0.000006193759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006950659,"about_ca_system_score_gemma":0.0003869667,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3734629,"about_ca_topic_score_gemma":0.04214615,"domain_scores_codex":[0.9939411,0.0006237695,0.001544054,0.0004901517,0.002971476,0.0004294847],"domain_scores_gemma":[0.9954853,0.001273097,0.001818672,0.0007081088,0.0005851635,0.000129664],"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":[0.0001510469,0.00004162307,0.0001620554,0.00004285441,0.00005007265,1.06189e-7,0.001914149,0.03624734,0.002187907,0.00001274884,0.00002612793,0.959164],"study_design_scores_gemma":[0.00106445,0.0004250079,0.004906233,0.0003454211,0.00003945389,0.00004053928,0.0007890481,0.9777539,0.01120443,0.00263013,0.0005322863,0.000269142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06030945,0.00001173075,0.9260811,0.003351417,0.002242688,0.0009081554,0.00004029728,0.00009678624,0.006958391],"genre_scores_gemma":[0.9948997,0.00002650843,0.003704547,0.001240445,0.00006455616,5.205627e-7,0.00002101174,0.000008249356,0.00003446288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9588948,"threshold_uncertainty_score":0.999846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0204403466854158,"score_gpt":0.2851846017056102,"score_spread":0.2647442550201944,"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."}}