{"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 œinternational 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000486149,0.0004478257,0.0004805051,0.003167644,0.0002116925,0.0006827199,0.0003355385,0.0003462984,0.0006493299],"category_scores_gemma":[0.001504836,0.000202047,0.0003741539,0.001006136,0.0002093303,0.000722968,0.0004705142,0.0002541884,0.0001704419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00031385,"about_ca_system_score_gemma":0.0003979362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003260867,"about_ca_topic_score_gemma":0.002802069,"domain_scores_codex":[0.9995052,0.00007686116,0.00002289604,0.00007193276,0.0002397329,0.00008338435],"domain_scores_gemma":[0.9991203,0.00017894,0.0001400267,0.00006936032,0.0004104768,0.00008095721],"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":[0.001591507,0.000411511,0.06083024,0.0004757189,0.0001873859,0.0007501023,0.0004795246,0.08273935,0.3193652,0.001874265,0.002852195,0.5284431],"study_design_scores_gemma":[0.00003389913,0.0002556101,0.06003458,0.00002237939,0.00003987866,0.0002647269,0.0001782964,0.8969569,0.04098098,0.0006090206,0.0005809262,0.00004288838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8428347,0.0003020574,0.15379,0.00006807533,0.00005236998,0.00008406879,0.0003175375,0.0009520929,0.001599041],"genre_scores_gemma":[0.9854385,0.00006157794,0.01408183,0.000006309352,0.00001002787,0.00001138671,0.0002161621,0.00001700947,0.000157161],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003260867,"threshold_uncertainty_score":0.006483793,"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."}}