{"id":"W2547697372","doi":"10.1109/igarss.2016.7729396","title":"Object detection in pleiades images using deep features","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Effigis (Canada); Computer Research Institute of Montréal","funders":"","keywords":"Pleiades; Computer science; Artificial intelligence; Convolutional neural network; Deep learning; Exploit; Object detection; Computer vision; Object (grammar); Deep neural networks; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology)","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":[0.0002553582,0.0004501508,0.0003157531,0.001262939,0.0001771529,0.0005105316,0.0003729498,0.0004087353,0.001254164],"category_scores_gemma":[0.0004698343,0.0003030367,0.0003959536,0.0004924538,0.0002441806,0.0007596684,0.0005321134,0.000422588,0.0004593856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949492,"about_ca_system_score_gemma":0.0002496497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004881994,"about_ca_topic_score_gemma":0.01096327,"domain_scores_codex":[0.9998372,0.00001706308,0.000004423532,0.00004730202,0.00006226943,0.00003179083],"domain_scores_gemma":[0.9998025,0.00006233197,0.0000339807,0.00003020105,0.00004805387,0.00002300297],"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":[0.0006778004,0.000349162,0.02097523,0.0002572817,0.0002167982,0.0006163908,0.0002537414,0.08281538,0.2793321,0.001670265,0.00545782,0.6073781],"study_design_scores_gemma":[0.00002901188,0.0001497132,0.04227554,0.00002866237,0.00004968511,0.0003947303,0.0001303868,0.874041,0.07708673,0.002008611,0.003774571,0.00003145341],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6757923,0.001261039,0.3122506,0.0004528359,0.00005614832,0.00007680211,0.001277869,0.004012843,0.004819604],"genre_scores_gemma":[0.8053486,0.0004309632,0.1892028,0.00009685809,0.00002590513,0.00002468589,0.001930858,0.00008055712,0.002858814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004881994,"threshold_uncertainty_score":0.009707212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614770466482416,"score_gpt":0.2742768634363784,"score_spread":0.2581291587715542,"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."}}