{"id":"W2945842616","doi":"10.1016/j.dib.2019.103962","title":"Unmanned aerial image dataset: Ready for 3D reconstruction","year":2019,"lang":"en","type":"article","venue":"Data in Brief","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Centre de Géomatique du Québec; Université de Sherbrooke; University of Calgary","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Photogrammetry; Point cloud; Computer science; Structure from motion; Workflow; Computer vision; Geospatial analysis; Polygon mesh; Ground truth; Artificial intelligence; Laser scanning; Terrain; 3D reconstruction; Remote sensing; Level of detail; Scanner; 3D modeling; Computer graphics (images); Geology; Geography; Cartography; Motion (physics); Database","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":[0.0004421308,0.002258874,0.001095856,0.002552296,0.0006506373,0.0009858871,0.001882419,0.001466234,0.01020536],"category_scores_gemma":[0.0009983686,0.0004722546,0.0009851881,0.003369119,0.0004628483,0.0008290677,0.001468638,0.001509828,0.02192414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005836783,"about_ca_system_score_gemma":0.001103095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01425026,"about_ca_topic_score_gemma":0.04022273,"domain_scores_codex":[0.9993078,0.0000415129,0.00005557318,0.0001752654,0.0003104635,0.0001094412],"domain_scores_gemma":[0.9994231,0.00004828586,0.00005345405,0.0001926377,0.0002198608,0.00006268775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003609439,0.0004062986,0.005926094,0.001492197,0.0002073775,0.0007062337,0.0001459836,0.00662072,0.01372889,0.000714627,0.9082329,0.06145769],"study_design_scores_gemma":[0.0004060163,0.0002204134,0.05519763,0.000454494,0.0001212472,0.00131254,0.0007755107,0.01946007,0.01772812,0.002746304,0.9013508,0.0002269265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.008217505,0.0005212036,0.004713537,0.0001681402,0.0001648573,0.0001799373,0.9784404,0.004285655,0.003308719],"genre_scores_gemma":[0.006953161,0.0001329821,0.007786299,0.00004591225,0.00001546529,0.0001443315,0.984158,0.0001648923,0.000598773],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01425026,"threshold_uncertainty_score":0.03414035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05305299323025388,"score_gpt":0.2755481976235086,"score_spread":0.2224952043932547,"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."}}