{"id":"W2755907046","doi":"10.5194/isprs-archives-xlii-2-w7-269-2017","title":"CLUSTERING OF MULTISPECTRAL AIRBORNE LASER SCANNING DATA USING GAUSSIAN DECOMPOSITION","year":2017,"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":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multispectral image; Remote sensing; Lidar; Gaussian; Histogram; Terrain; Cluster analysis; Computer science; Laser scanning; Silhouette; Environmental science; Artificial intelligence; Geography; Laser; Optics; Physics; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0008050646,0.0005936247,0.0005762142,0.003556198,0.0004230707,0.0006850766,0.0005554024,0.0004405708,0.0005087344],"category_scores_gemma":[0.00169223,0.000259613,0.0009703143,0.002438697,0.0003658761,0.0005135148,0.0005898033,0.0003847305,0.0004144927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008014949,"about_ca_system_score_gemma":0.0008190797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01341804,"about_ca_topic_score_gemma":0.01041569,"domain_scores_codex":[0.9992397,0.0001337303,0.0000483855,0.0001802727,0.000312978,0.00008501456],"domain_scores_gemma":[0.9990755,0.0002101625,0.0001077387,0.0001060279,0.0004597552,0.00004073499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003351955,0.00025756,0.0133164,0.0002165682,0.0002386115,0.0002261989,0.0005248148,0.2927018,0.06697562,0.002979807,0.003030618,0.6191968],"study_design_scores_gemma":[0.000005490159,0.00002743755,0.005949027,0.000006754035,0.00001496419,0.00004352397,0.00006741592,0.9868421,0.005655289,0.000804734,0.0005672664,0.00001598352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1027035,0.000138379,0.8952997,0.00005308036,0.00002369553,0.00007399211,0.0002157405,0.001027285,0.00046465],"genre_scores_gemma":[0.5865456,0.0001619422,0.4109022,0.00003636467,0.00002532188,0.0001079986,0.001185999,0.0001043586,0.0009303014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01341804,"threshold_uncertainty_score":0.02667987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02891322352366616,"score_gpt":0.2962524501707491,"score_spread":0.267339226647083,"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."}}