{"id":"W2976989770","doi":"10.5194/isprs-annals-iv-4-w8-139-2019","title":"AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH","year":2019,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Lidar; Point cloud; Segmentation; Pointwise; Computer science; Remote sensing; Artificial intelligence; Computer vision; Random forest; Point (geometry); Mobile mapping; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003553521,0.0007278773,0.0008914198,0.003345601,0.0005803107,0.0009328168,0.0008940639,0.000854407,0.001177013],"category_scores_gemma":[0.0006324349,0.0003642389,0.001215751,0.001955713,0.0004670139,0.001072514,0.0009188175,0.0005900785,0.0007797944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004671098,"about_ca_system_score_gemma":0.0008599902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004962622,"about_ca_topic_score_gemma":0.007079874,"domain_scores_codex":[0.9994685,0.00005319503,0.00003522195,0.0001472276,0.00021201,0.00008390929],"domain_scores_gemma":[0.999635,0.00005501639,0.00004509357,0.00005647134,0.0001830712,0.0000254504],"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":[0.0002259632,0.0002202359,0.006194602,0.0002192758,0.000118222,0.0002306409,0.0001888546,0.09016439,0.1043468,0.003498337,0.004125347,0.7904674],"study_design_scores_gemma":[0.0000100485,0.00004476632,0.003354879,0.00001496291,0.00002342196,0.0001641504,0.00009161141,0.974981,0.01754465,0.001909525,0.001836525,0.00002440871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04257959,0.0001559762,0.9539585,0.0000779668,0.00003304251,0.00009083949,0.0002424404,0.001810998,0.001050612],"genre_scores_gemma":[0.3442876,0.0001597634,0.6528226,0.00008871833,0.0000406972,0.0001168832,0.001290434,0.0001913637,0.001001875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004962622,"threshold_uncertainty_score":0.009867489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01558861071578398,"score_gpt":0.2619034203432477,"score_spread":0.2463148096274637,"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."}}