{"id":"W1840686580","doi":"10.5194/isprsarchives-xl-1-w4-313-2015","title":"UAV-BASED POINT CLOUD GENERATION FOR OPEN-PIT MINE MODELLING","year":2015,"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":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Géomatique du Québec; York University; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Photogrammetry; Point cloud; Bundle adjustment; Computer science; Structure from motion; Computer vision; Artificial intelligence; Software; Matching (statistics); Inertial measurement unit; Camera resectioning; Calibration; Inertial navigation system; Inertial frame of reference; Motion (physics)","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":[0.0001707954,0.0005178494,0.0005055768,0.0009107413,0.0003403956,0.0005176994,0.0008109575,0.0005166573,0.003328361],"category_scores_gemma":[0.0007775049,0.0003290554,0.0005067574,0.0009463261,0.0001484198,0.0005778541,0.0007211809,0.000428773,0.001106733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000368723,"about_ca_system_score_gemma":0.0005163941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007053562,"about_ca_topic_score_gemma":0.008283067,"domain_scores_codex":[0.9998306,0.00002300974,0.000008962075,0.00002820081,0.00008933323,0.00001988564],"domain_scores_gemma":[0.9997495,0.00004184006,0.00002095045,0.00006354509,0.0001081118,0.00001604018],"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":[0.0002713489,0.00009919829,0.004298706,0.0001607341,0.00007725335,0.0004372954,0.0001761071,0.5466821,0.03756015,0.003801497,0.006922938,0.3995126],"study_design_scores_gemma":[0.000008388129,0.00001066974,0.00053378,0.000004525367,0.0000031749,0.00003519316,0.0000153847,0.9942042,0.003583457,0.0005090414,0.001087317,0.000004835344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.050423,0.0001411221,0.9421493,0.00008589581,0.00005958436,0.0001459814,0.0006916138,0.004104902,0.002198556],"genre_scores_gemma":[0.5761688,0.0001444245,0.4203427,0.00002626215,0.0000154608,0.0001247379,0.001686428,0.0001717752,0.001319391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007053562,"threshold_uncertainty_score":0.01402497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05503380958655039,"score_gpt":0.267273549453317,"score_spread":0.2122397398667666,"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."}}