{"id":"W3177354419","doi":"10.5194/isprs-archives-xliii-b1-2021-31-2021","title":"AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS","year":2021,"lang":"en","type":"article","venue":"The international 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":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Computer science; Artificial intelligence; Lidar; Laser scanning; Convolutional neural network; Deep learning; Classifier (UML); Terrain; Binary classification; Pattern recognition (psychology); Artificial neural network; Feature extraction; Feature (linguistics); Data mining; Remote sensing; Computer vision; Support vector machine; Laser; 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":[0.0004027372,0.0007686195,0.0007668231,0.001249355,0.0004595021,0.0007567544,0.001458028,0.0009670169,0.002370924],"category_scores_gemma":[0.0008074575,0.0003941777,0.0008416023,0.001244584,0.0003001369,0.0008520777,0.001017052,0.001166301,0.001122572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016514,"about_ca_system_score_gemma":0.001058181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01880367,"about_ca_topic_score_gemma":0.02371835,"domain_scores_codex":[0.999663,0.00002745337,0.00001784109,0.00007793449,0.0001384496,0.00007530954],"domain_scores_gemma":[0.9996848,0.00006492026,0.00003024712,0.00004147493,0.0001533728,0.00002511758],"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":[0.0001414794,0.0001560737,0.001908236,0.0000775766,0.00006615607,0.00009339925,0.00005584852,0.2643062,0.01397466,0.002872412,0.005295333,0.7110526],"study_design_scores_gemma":[0.000003375884,0.000009983191,0.000194716,0.00000273613,0.000003385963,0.000009492211,0.000006104768,0.9972161,0.001587453,0.0006328177,0.0003314547,0.000002336197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03601503,0.0002670954,0.9592675,0.0001708964,0.00005113229,0.00007180018,0.0002420161,0.002641919,0.001272667],"genre_scores_gemma":[0.5043601,0.0002827531,0.4874985,0.0002475372,0.00006905562,0.0001581047,0.001995736,0.0001961228,0.005192111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01880367,"threshold_uncertainty_score":0.03738838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01395505236468708,"score_gpt":0.2512539965993774,"score_spread":0.2372989442346903,"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."}}