{"id":"W4378194604","doi":"10.5194/isprs-archives-xlviii-1-w1-2023-93-2023","title":"THE IMPLEMENTATION OF SEMI-AUTOMATED ROAD SURFACE MARKINGS EXTRACTION SCHEMES UTILIZING MOBILE LASER SCANNED POINT CLOUDS FOR HD MAPS PRODUCTION","year":2023,"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":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Computer science; Digitization; Computer vision; Road surface; Artificial intelligence; Mobile mapping; Filter (signal processing); Laser scanning; Point (geometry); Remote sensing; Laser; Geography; Engineering","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":[0.0003439888,0.0006086587,0.0003864417,0.001321834,0.0003591887,0.0006915274,0.0008688709,0.0004515256,0.001679265],"category_scores_gemma":[0.0008113006,0.0003367503,0.0005874806,0.000888088,0.0002024174,0.000847657,0.000515912,0.0003308021,0.0007997257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003078898,"about_ca_system_score_gemma":0.0007884038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00622831,"about_ca_topic_score_gemma":0.005087813,"domain_scores_codex":[0.9996138,0.00004747044,0.00002072029,0.00006722798,0.0002063328,0.00004440753],"domain_scores_gemma":[0.9995387,0.00006431006,0.00004106133,0.0001001344,0.0002290765,0.00002668787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002476663,0.0001742059,0.005664002,0.0001957578,0.00008993696,0.0002772037,0.0002253842,0.0742299,0.1864459,0.002694838,0.003529901,0.7262254],"study_design_scores_gemma":[0.00001791099,0.00007690719,0.004322556,0.00001184633,0.00002035255,0.0001221551,0.00006590668,0.9306226,0.06111682,0.0006091358,0.002984527,0.00002927415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05197527,0.0000784377,0.9434859,0.00004755568,0.00004427953,0.0001058599,0.0001636896,0.00308002,0.001019165],"genre_scores_gemma":[0.4631157,0.0001224876,0.534682,0.00003176586,0.00001572971,0.0001046986,0.0005449509,0.00008946477,0.001293181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00622831,"threshold_uncertainty_score":0.01238412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473741910086175,"score_gpt":0.2862489329505813,"score_spread":0.2715115138497196,"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."}}