{"id":"W4281702131","doi":"10.5194/isprs-archives-xliii-b1-2022-59-2022","title":"ROBUST APPROACH FOR URBAN ROAD SURFACE EXTRACTION USING MOBILE LASER SCANNING 3D POINT CLOUDS","year":2022,"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":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Road surface; Point cloud; Outlier; Laser scanning; Noise (video); Computer science; Point (geometry); Artificial intelligence; Computer vision; Engineering; Laser; Mathematics; Image (mathematics); Civil 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":[0.0005573297,0.00138907,0.001191433,0.004719861,0.0005458333,0.001210859,0.001398899,0.001078406,0.001641973],"category_scores_gemma":[0.001184811,0.0007605075,0.001660728,0.002848455,0.0004394624,0.001167223,0.001102404,0.0009978,0.001982867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006243013,"about_ca_system_score_gemma":0.001179124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006157843,"about_ca_topic_score_gemma":0.006749365,"domain_scores_codex":[0.9987034,0.00009750929,0.00006020697,0.0002421826,0.0007704673,0.0001262287],"domain_scores_gemma":[0.9992499,0.00009077039,0.00009498485,0.000152619,0.000381679,0.0000300731],"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":[0.0002015784,0.0001749274,0.005185086,0.0003084092,0.0002488364,0.0003311433,0.0001904628,0.1741544,0.1517217,0.00296833,0.005565356,0.6589499],"study_design_scores_gemma":[0.00001339893,0.00003794393,0.003126565,0.00001192284,0.00003136679,0.0001276097,0.00008149034,0.9616756,0.03094468,0.001049873,0.002861475,0.0000380241],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0274322,0.0002624327,0.966961,0.0001068286,0.00004351406,0.000100424,0.0003158889,0.003969372,0.0008083333],"genre_scores_gemma":[0.3115906,0.0003556946,0.6843401,0.000102349,0.00004493476,0.0001744879,0.001796781,0.0003475188,0.00124758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006157843,"threshold_uncertainty_score":0.01224399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290420641726024,"score_gpt":0.2589781171937335,"score_spread":0.2360739107764732,"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."}}