{"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 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":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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001534702,0.0003336675,0.0004442411,0.0004524391,0.0008759719,0.0002701799,0.001046715,0.00009215479,0.00001682701],"category_scores_gemma":[0.000632116,0.000228566,0.0003964707,0.001018983,0.002935275,0.0004044841,0.0007914933,0.000272516,0.000002750804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005293309,"about_ca_system_score_gemma":0.0002076011,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5477775,"about_ca_topic_score_gemma":0.1537129,"domain_scores_codex":[0.9957331,0.0002875882,0.001596481,0.0003991409,0.001567414,0.0004162519],"domain_scores_gemma":[0.9960968,0.0009452855,0.002017352,0.000505953,0.0003035518,0.0001309934],"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":[0.0001165289,0.00002636938,0.0007707391,0.00007013705,0.00007274738,1.174761e-7,0.005901959,0.004150747,0.03497153,0.00001189103,0.00008795594,0.9538193],"study_design_scores_gemma":[0.0007563818,0.0001200113,0.006780209,0.0002615523,0.00006158465,0.00006855634,0.003334578,0.8807819,0.09856173,0.007598588,0.001460322,0.0002145345],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07295886,0.00002028417,0.9166814,0.003626283,0.0009054465,0.000733458,0.0001724041,0.00002585261,0.004876005],"genre_scores_gemma":[0.9851565,0.00009059517,0.01373805,0.0007393777,0.00007583235,4.608285e-7,0.00008651474,0.00001174009,0.0001009404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9536048,"threshold_uncertainty_score":0.9997782,"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."}}