{"id":"W4200196735","doi":"10.5194/isprs-archives-xlvi-4-w5-2021-397-2021","title":"INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS","year":2021,"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":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Segmentation; Computer science; Artificial intelligence; Block (permutation group theory); Scale (ratio); Point (geometry); Computer vision; Pattern recognition (psychology); Geography; Mathematics; Cartography; Geometry","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":[0.001433807,0.001760264,0.00119626,0.003066726,0.0007784112,0.001668857,0.002223744,0.001548098,0.002215303],"category_scores_gemma":[0.003620376,0.0004975425,0.0009657058,0.002328749,0.0007468282,0.002314514,0.001352414,0.0009291255,0.001031255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001883195,"about_ca_system_score_gemma":0.001573143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02845978,"about_ca_topic_score_gemma":0.03095204,"domain_scores_codex":[0.9988962,0.0001720083,0.00006441877,0.0003110447,0.0003905063,0.0001657812],"domain_scores_gemma":[0.9986759,0.0004262176,0.0001127227,0.0001850004,0.0004886581,0.000111455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008896353,0.0004091554,0.01849163,0.0005889671,0.0004115092,0.000512697,0.0003229271,0.57823,0.01915129,0.005643388,0.01691879,0.35843],"study_design_scores_gemma":[0.00001507495,0.00005935769,0.001691661,0.00001480818,0.00001520145,0.00005543092,0.00008842492,0.9903217,0.005364545,0.001102226,0.001260638,0.00001094792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6440538,0.002209531,0.3108091,0.001150814,0.0005562835,0.0006230677,0.004774675,0.02663789,0.009184992],"genre_scores_gemma":[0.8037645,0.000371267,0.1815647,0.0002659389,0.00005667882,0.0001833302,0.01142065,0.0005040212,0.001868805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02845978,"threshold_uncertainty_score":0.05658829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609933956932818,"score_gpt":0.2572082999023531,"score_spread":0.2411089603330249,"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."}}