{"id":"W2625290171","doi":"10.1109/lgrs.2017.2707467","title":"A Fast Edge Extraction Method for Mobile Lidar Point Clouds","year":2017,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"China Scholarship Council; University of Calgary","keywords":"Lidar; Computer science; Point cloud; Enhanced Data Rates for GSM Evolution; Ranging; Edge detection; Point (geometry); Computer vision; Feature extraction; Artificial intelligence; Extraction (chemistry); Remote sensing; Mathematics; Image processing; Geometry; Image (mathematics); Geology","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":[0.0001788768,0.0006683548,0.0005580115,0.001897386,0.0004515413,0.0006501033,0.0006852562,0.000666653,0.001410851],"category_scores_gemma":[0.0006838249,0.0004341649,0.000588265,0.001454779,0.0002212604,0.001212405,0.0007003113,0.0006484063,0.000899443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002114571,"about_ca_system_score_gemma":0.0003852285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001418628,"about_ca_topic_score_gemma":0.001540704,"domain_scores_codex":[0.9997105,0.00002183858,0.0000166485,0.00006954904,0.0001486198,0.00003273848],"domain_scores_gemma":[0.9996705,0.00007043366,0.00003260743,0.00004259894,0.0001627041,0.0000211103],"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":[0.0001301313,0.00005002773,0.001012167,0.0001746008,0.00003615416,0.0002884346,0.0001046369,0.02193055,0.1442252,0.003374222,0.003895188,0.8247786],"study_design_scores_gemma":[0.00003276764,0.0001118694,0.002519125,0.00002966111,0.00003179989,0.0006912776,0.00009088401,0.8651398,0.115049,0.004152409,0.01207513,0.00007632661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01742319,0.0002858954,0.9805312,0.00005177048,0.00006669151,0.00005609689,0.0001066584,0.0009658831,0.0005126797],"genre_scores_gemma":[0.1333959,0.000415877,0.8637076,0.00006494882,0.00005543845,0.00009238768,0.0005480637,0.0001624609,0.001557281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001897386,"threshold_uncertainty_score":0.004719734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550844195944184,"score_gpt":0.2922389306202173,"score_spread":0.2767304886607755,"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."}}