{"id":"W4395684896","doi":"10.1016/j.jag.2024.103862","title":"Semantic segmentation of large-scale point cloud scenes via dual neighborhood feature and global spatial-aware","year":2024,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Point cloud; Computer science; Segmentation; Artificial intelligence; Pattern recognition (psychology); Spatial analysis; Computer vision; Geography; Remote sensing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003243598,0.001337567,0.001306884,0.002663863,0.0007824185,0.001175982,0.001937169,0.0009811537,0.001447393],"category_scores_gemma":[0.0009420908,0.0005621197,0.00143639,0.002464607,0.0007532581,0.001896554,0.001900409,0.0008209594,0.0009203806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097225,"about_ca_system_score_gemma":0.001304051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01460427,"about_ca_topic_score_gemma":0.0272562,"domain_scores_codex":[0.9995667,0.0000251282,0.00001921041,0.0001829798,0.0001334269,0.00007246006],"domain_scores_gemma":[0.9997259,0.0000445916,0.00004140807,0.00006024809,0.00009541951,0.0000324971],"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":[0.0005051871,0.0003185627,0.00760216,0.0002613445,0.0001980896,0.0003525112,0.0004461006,0.2867926,0.05864222,0.00807956,0.01035259,0.626449],"study_design_scores_gemma":[0.0000160618,0.00004308404,0.001865886,0.00001344318,0.00003175467,0.00009958709,0.00007949724,0.9804102,0.009610965,0.005517305,0.00229469,0.00001748816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06834033,0.0003862343,0.9226118,0.000174652,0.0000460662,0.0001425445,0.0007628122,0.005455037,0.002080548],"genre_scores_gemma":[0.4646806,0.0003492922,0.5255561,0.00021299,0.0000625288,0.0002363768,0.005375866,0.0006028112,0.002923511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01460427,"threshold_uncertainty_score":0.02903855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005854723200745882,"score_gpt":0.2213231290633694,"score_spread":0.2154684058626235,"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."}}