{"id":"W3129278271","doi":"10.1109/igarss39084.2020.9323218","title":"Extraction of Power Lines and Pylons from LiDAR Point Clouds Using a GCN-Based Method","year":2020,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Point cloud; Lidar; Computer science; Reliability (semiconductor); Power (physics); Line (geometry); Artificial intelligence; Feature extraction; Real-time computing; Key (lock); Remote sensing; Geography; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00008608955,0.00006750634,0.00009722404,0.00001030536,0.00005487803,0.00001395801,0.00004749549,0.00004018112,0.0007989124],"category_scores_gemma":[0.00003315378,0.0000584903,0.00003294764,0.0001336516,0.00006106749,0.00005356428,0.00003375904,0.00006141343,0.00003372676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001891315,"about_ca_system_score_gemma":0.000008362666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002185229,"about_ca_topic_score_gemma":0.00003559438,"domain_scores_codex":[0.9994411,0.00003959552,0.0001354228,0.0001975254,0.0001085596,0.00007778993],"domain_scores_gemma":[0.9996392,0.00009035297,0.0000571762,0.0001284647,0.000006164888,0.00007859965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001719084,0.00003779468,0.002872771,0.000002784326,0.000007402946,0.00000103255,0.0004737994,0.002737624,0.983449,0.00003460016,0.0002960428,0.01006991],"study_design_scores_gemma":[0.0005255152,0.0001087479,0.07892023,0.00001952972,0.00007246355,0.000008611611,0.0006751561,0.5826147,0.3203571,0.0007163761,0.015684,0.0002975699],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.711413,0.00001377979,0.2824569,0.001684445,0.00002064769,0.0000753165,0.000006108316,0.00003239402,0.004297362],"genre_scores_gemma":[0.7566227,0.000001487044,0.2429178,0.0003956038,0.00002007774,2.332503e-7,0.000002519372,0.000006162518,0.0000334626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6630919,"threshold_uncertainty_score":0.874753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02577796225990888,"score_gpt":0.2929987333961954,"score_spread":0.2672207711362866,"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."}}