{"id":"W2165036263","doi":"10.5194/isprsarchives-xl-5-55-2014","title":"Segmentation of planar surfaces in LiDAR point clouds of an electrical substation by exploring the structure of points neighbourhood","year":2014,"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":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Lidar; Segmentation; Planar; Electrical conductor; Computer science; Neighbourhood (mathematics); Point (geometry); Plan (archaeology); Geography; Remote sensing; Electrical engineering; Artificial intelligence; Engineering; Computer graphics (images); Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002966806,0.0005677895,0.0004734753,0.003251598,0.0004637906,0.001063711,0.0006012213,0.0007456917,0.0005689735],"category_scores_gemma":[0.0009729188,0.0003629503,0.000665266,0.002110578,0.0003186967,0.0005812586,0.0007965488,0.0004186835,0.0005440846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004431276,"about_ca_system_score_gemma":0.0008000066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0133792,"about_ca_topic_score_gemma":0.02382937,"domain_scores_codex":[0.9995839,0.00002811942,0.00002325377,0.00009823743,0.0001745754,0.00009200309],"domain_scores_gemma":[0.9995697,0.0001072435,0.00005586581,0.00005960136,0.0001813162,0.0000261679],"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":[0.0004626811,0.0003392031,0.06712953,0.0003903474,0.0001732397,0.001546846,0.001451889,0.1820536,0.2151081,0.001699236,0.006718426,0.522927],"study_design_scores_gemma":[0.00001225028,0.0000674018,0.06242547,0.00003446884,0.00003263659,0.0004139636,0.0009728281,0.8923246,0.03924596,0.001092623,0.003338484,0.00003924118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7760104,0.0004926695,0.2168525,0.0002031004,0.00004534481,0.0002382601,0.001482539,0.002270828,0.00240439],"genre_scores_gemma":[0.8631351,0.0002569872,0.1329284,0.00002868389,0.00001577881,0.00006784916,0.002713427,0.00008979686,0.0007641101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0133792,"threshold_uncertainty_score":0.02660269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227064875642966,"score_gpt":0.239437161362305,"score_spread":0.2271665126058754,"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."}}