{"id":"W2981084407","doi":"10.5194/isprs-archives-xlii-4-w18-843-2019","title":"AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS","year":2019,"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":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"Point cloud; Lidar; Photogrammetry; Computer science; Artificial intelligence; Visibility; Deep learning; Machine learning; Point (geometry); Structure from motion; 3D city models; Aerial imagery; Visualization; Algorithm; Remote sensing; Geography; Motion (physics); Meteorology; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001256634,0.0004149754,0.0003993244,0.0004903996,0.001558296,0.0007059968,0.00105743,0.00009769134,0.00002711458],"category_scores_gemma":[0.000426841,0.0002581729,0.0002258465,0.0008784566,0.003102308,0.0004910749,0.0009303099,0.0006238888,0.0000123753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006384898,"about_ca_system_score_gemma":0.00009842703,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6988022,"about_ca_topic_score_gemma":0.1320088,"domain_scores_codex":[0.995856,0.0003549853,0.001108334,0.0005034435,0.001680646,0.0004966072],"domain_scores_gemma":[0.9969487,0.0008317646,0.001500867,0.0004062892,0.0001433441,0.0001690994],"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":[0.0001590696,0.00001678674,0.001655715,0.00002312341,0.00005736059,2.296504e-7,0.003202229,0.006248148,0.004418334,0.000007754752,0.00001484631,0.9841964],"study_design_scores_gemma":[0.0008113069,0.0002140827,0.008338078,0.0001907565,0.00004362786,0.0002130434,0.002593755,0.9724767,0.00349802,0.001853785,0.009445825,0.0003210252],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04388907,0.00002718443,0.9330443,0.003545807,0.001026074,0.0007666204,0.00003112468,0.00006256785,0.0176073],"genre_scores_gemma":[0.9939352,0.0001948404,0.004950906,0.0004948576,0.0001221283,3.498969e-7,0.00004618399,0.00001565673,0.0002398848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9838754,"threshold_uncertainty_score":0.9999871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01265851293157213,"score_gpt":0.2373618732853666,"score_spread":0.2247033603537945,"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."}}