{"id":"W2471941257","doi":"10.5194/isprs-archives-xli-b1-741-2016","title":"3D LAND COVER CLASSIFICATION BASED ON MULTISPECTRAL LIDAR POINT CLOUDS","year":2016,"lang":"en","type":"article","venue":"˜The œinternational 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":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Geo-Information Engineering; University of Waterloo","keywords":"Multispectral image; Remote sensing; Lidar; Point cloud; Land cover; Computer science; Environmental science; Artificial intelligence; Geography; Land use","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":[0.0001890035,0.0005803049,0.0004186785,0.004794104,0.000343037,0.001435569,0.0003416696,0.0004136919,0.001077621],"category_scores_gemma":[0.0003289466,0.0002528346,0.0008263218,0.001660328,0.0001812155,0.0007218678,0.0005315588,0.000283255,0.0008293359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003656343,"about_ca_system_score_gemma":0.000310803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006709826,"about_ca_topic_score_gemma":0.008104862,"domain_scores_codex":[0.9996159,0.00002990396,0.00002063418,0.00006990818,0.00021063,0.00005293848],"domain_scores_gemma":[0.9998136,0.0000204063,0.00003177666,0.00002536434,0.00009069695,0.00001812262],"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":[0.0003351052,0.0003365557,0.06096969,0.000322089,0.0002210386,0.000943021,0.0003923067,0.1180759,0.1408761,0.002203715,0.005236663,0.6700878],"study_design_scores_gemma":[0.0000143284,0.0000689556,0.07205642,0.00006223674,0.000059073,0.0003225979,0.0004022353,0.8930475,0.02862166,0.001231573,0.004057895,0.00005566674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5637084,0.0007593099,0.4199896,0.0002249565,0.0001277815,0.0003174271,0.003632698,0.003571521,0.007668246],"genre_scores_gemma":[0.8789909,0.0003461526,0.1149908,0.00005377756,0.00003229882,0.0001133177,0.003979766,0.00006757591,0.001425366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006709826,"threshold_uncertainty_score":0.01334155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0152924951829141,"score_gpt":0.2474159759790343,"score_spread":0.2321234807961202,"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."}}