{"id":"W2948640278","doi":"10.5194/isprs-archives-xlii-2-w13-553-2019","title":"A CALIBRATION WORKFLOW FOR “PROSUMER” UAV CAMERAS","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":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Videography; Quadcopter; Photogrammetry; Computer science; Software; Computer vision; Calibration; Artificial intelligence; Workflow; Camera resectioning; Bundle adjustment; Orientation (vector space); Digital camera; Drone; Distortion (music); Computer graphics (images); Remote sensing; Engineering; Geography; Database","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002080136,0.00149066,0.0006418512,0.002008731,0.001144869,0.002074297,0.001736252,0.0009068046,0.02041334],"category_scores_gemma":[0.0045608,0.001325573,0.001138097,0.0009378104,0.0005946457,0.001694849,0.002890008,0.00192367,0.01342453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00133698,"about_ca_system_score_gemma":0.001785934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004663482,"about_ca_topic_score_gemma":0.006218,"domain_scores_codex":[0.9983308,0.0002012783,0.000165197,0.0004968561,0.0006405822,0.0001652732],"domain_scores_gemma":[0.9975025,0.0004464092,0.0001543229,0.0008098457,0.000867072,0.0002198239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003221711,0.000236825,0.004295933,0.0006461818,0.00009353708,0.001793996,0.002440065,0.01735081,0.07541931,0.01665245,0.06287663,0.817872],"study_design_scores_gemma":[0.0002254392,0.000220181,0.0165587,0.000769774,0.00008126647,0.003533124,0.002092914,0.2161093,0.2018092,0.04098474,0.5170945,0.0005209257],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007994586,0.0002989668,0.9234838,0.0002504585,0.00008287634,0.0005912578,0.001367912,0.0552089,0.01072122],"genre_scores_gemma":[0.08882277,0.0004472879,0.8843933,0.0002859982,0.00004773933,0.0004573366,0.006160631,0.01005584,0.009329141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02041334,"threshold_uncertainty_score":0.0682894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731877400569663,"score_gpt":0.2376728236888835,"score_spread":0.2203540496831869,"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."}}