{"id":"W3048719189","doi":"10.5194/isprs-archives-xliii-b2-2020-247-2020","title":"SIMULATION-BASED DATA AUGMENTATION USING PHYSICAL PRIORS FOR NOISE FILTERING DEEP NEURAL NETWORK","year":2020,"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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada); York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lidar; Point cloud; Noise (video); Artificial intelligence; Artificial neural network; Deep learning; Computer vision; Remote sensing; Image (mathematics); Geography","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":[0.001313407,0.001470551,0.0008567758,0.0007950928,0.0004383098,0.0009615408,0.001735688,0.001360852,0.001736642],"category_scores_gemma":[0.005188994,0.0006408726,0.001400834,0.0008150424,0.0008703359,0.00134026,0.001258505,0.002195174,0.000522352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431544,"about_ca_system_score_gemma":0.001230074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01783008,"about_ca_topic_score_gemma":0.01657921,"domain_scores_codex":[0.9994405,0.0001708639,0.00003599327,0.0001560613,0.000132473,0.00006407884],"domain_scores_gemma":[0.9981791,0.0009950054,0.0001492947,0.0002235581,0.0003864256,0.0000666847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001666435,0.00006646695,0.001811542,0.00005319116,0.00005244272,0.00005120097,0.00002411428,0.9710496,0.001238403,0.0005660569,0.001118992,0.02380139],"study_design_scores_gemma":[0.0000034295,0.00001365014,0.000100121,0.000003862965,0.000003539147,0.000004496915,0.000002907525,0.9986501,0.000821567,0.0002747638,0.0001190244,0.000002513607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.296777,0.001515572,0.6825089,0.001408175,0.0004669571,0.0001890398,0.001986136,0.01108996,0.004058246],"genre_scores_gemma":[0.8498338,0.0002866778,0.1430782,0.0004211174,0.00004343772,0.0002223306,0.003868204,0.0002704157,0.001975805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01783008,"threshold_uncertainty_score":0.0354526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03664937743379886,"score_gpt":0.2900027619268414,"score_spread":0.2533533844930425,"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."}}