{"id":"W2902218628","doi":"10.1016/j.isprsjprs.2018.10.007","title":"A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds","year":2018,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":151,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Artificial intelligence; Road surface; Cluster analysis; Deep learning; Context (archaeology); Convolutional neural network; Laser scanning; Road traffic safety; Artificial neural network; Computer vision; Data mining; Pattern recognition (psychology); Transport engineering; Road traffic; Engineering; Geography","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":[0.0006497038,0.001038969,0.001278722,0.0008432158,0.0005539599,0.0009672559,0.002790173,0.001637173,0.002627941],"category_scores_gemma":[0.001018437,0.0009837112,0.001338508,0.00119169,0.0004585797,0.001155214,0.00167157,0.002132179,0.001749465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006489,"about_ca_system_score_gemma":0.001936088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0476329,"about_ca_topic_score_gemma":0.05666294,"domain_scores_codex":[0.9996123,0.00003641915,0.00002294722,0.0001276675,0.0001193309,0.00008134736],"domain_scores_gemma":[0.9996202,0.0000878978,0.00003314204,0.0000682746,0.0001559168,0.00003465794],"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":[0.0001550571,0.0002280218,0.0009330109,0.00008912609,0.0001077903,0.0000836098,0.00005304734,0.3350971,0.01002625,0.003519283,0.006816508,0.6428911],"study_design_scores_gemma":[0.00000448887,0.0000174075,0.0001610151,0.000004776596,0.000006416833,0.00001107811,0.000004634954,0.9970167,0.001204311,0.00103572,0.0005285778,0.000004818018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01622187,0.0004759777,0.9782244,0.0001664588,0.00006925207,0.00006617152,0.000468082,0.0035846,0.0007231835],"genre_scores_gemma":[0.3494986,0.0006325346,0.6361414,0.000307733,0.0001423157,0.0002588141,0.003439909,0.0002560747,0.009322522],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0476329,"threshold_uncertainty_score":0.0947113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650638479196665,"score_gpt":0.2831513596821171,"score_spread":0.2666449748901504,"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."}}