{"id":"W3090082674","doi":"10.3390/rs12193186","title":"Building Extraction from Airborne Multi-Spectral LiDAR Point Clouds Based on Graph Geometric Moments Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Point cloud; Computer science; Lidar; Convolutional neural network; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Discriminative model; Segmentation; Correctness; Computer vision; Data mining; Algorithm; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000249076,0.001229175,0.0007470303,0.001634973,0.0003076448,0.0005980714,0.00114641,0.000665954,0.001314566],"category_scores_gemma":[0.0006441766,0.0005307316,0.001079627,0.001256888,0.0003314641,0.0009846723,0.0007674504,0.0009081158,0.0008149314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007518521,"about_ca_system_score_gemma":0.0008026004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01542793,"about_ca_topic_score_gemma":0.02576747,"domain_scores_codex":[0.999691,0.00001970093,0.00001383917,0.0001001797,0.0001114675,0.00006390215],"domain_scores_gemma":[0.9997677,0.00004892453,0.00004145145,0.00005177469,0.00007510906,0.00001499141],"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":[0.0002007299,0.000141859,0.003031835,0.0001238387,0.0001639284,0.0001901525,0.00006216319,0.3415254,0.03663605,0.002339016,0.004423823,0.6111611],"study_design_scores_gemma":[0.000004155238,0.00001260792,0.0009044725,0.000005708278,0.00001735667,0.0000413992,0.000007948942,0.9886382,0.008934634,0.000851868,0.0005742736,0.000007426419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.114231,0.0007131582,0.8721777,0.0002069329,0.00007413356,0.00009089056,0.0008024833,0.009139544,0.002564196],"genre_scores_gemma":[0.6297756,0.0006491551,0.3593008,0.0002061934,0.00006168293,0.0001054951,0.004697303,0.0003148016,0.004888954],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01542793,"threshold_uncertainty_score":0.03067625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075979793171091,"score_gpt":0.252398212014924,"score_spread":0.2316384140832131,"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."}}