{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004694025,0.00007900532,0.0001197427,0.00002469346,0.00005598378,0.00007222509,0.0003557063,0.00004898532,0.002121355],"category_scores_gemma":[0.00007996171,0.00006558356,0.00001045426,0.00008603142,0.00003519213,0.000828061,0.00002560696,0.00007360848,0.0006582917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002774647,"about_ca_system_score_gemma":0.00002195577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003893994,"about_ca_topic_score_gemma":0.007025926,"domain_scores_codex":[0.9991817,0.00005253452,0.0001583433,0.0003175783,0.00009110378,0.0001987037],"domain_scores_gemma":[0.9992737,0.00008937596,0.00004411046,0.0005363916,0.00001348753,0.0000429293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005469533,0.00003771987,0.4523142,0.0001104306,0.00002574965,0.00001584044,0.0001337415,0.00005621746,0.0007966178,0.00006998734,0.1733957,0.3724969],"study_design_scores_gemma":[0.001301239,0.0001558741,0.2870916,0.00004057705,0.00001113317,0.00004234091,0.0002133709,0.01382006,0.00006982908,0.0001502958,0.696768,0.0003356581],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8546829,0.0001417916,0.00006720812,0.0001852792,0.002098621,0.0005360341,0.1360288,0.00005407685,0.006205251],"genre_scores_gemma":[0.4292156,0.00006583487,0.01530277,0.0004350346,0.000517424,0.000003499988,0.5537602,0.000008630709,0.0006909096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5233724,"threshold_uncertainty_score":0.9987909,"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."}}