{"id":"W3011688396","doi":"10.3390/rs12091432","title":"Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":289,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Energy; Athabasca University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Detector; Computer science; Residual; Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer vision; Generative adversarial network; Context (archaeology); Object detection; Overhead (engineering); Image resolution; Remote sensing; Deep learning; Pattern recognition (psychology); Telecommunications; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004487356,0.0004387387,0.0005180335,0.0002458192,0.0003313081,0.0004261012,0.0003168235,0.0001337291,6.666875e-7],"category_scores_gemma":[0.0004272856,0.0004372826,0.00005992942,0.001725776,0.0001238551,0.0005930422,0.0003305386,0.0005836948,0.000006377645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001809143,"about_ca_system_score_gemma":0.0001198699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002105703,"about_ca_topic_score_gemma":0.0003279294,"domain_scores_codex":[0.9970614,0.0002221168,0.0004340803,0.001153329,0.0003427508,0.0007863578],"domain_scores_gemma":[0.9985157,0.0002202904,0.0002419871,0.0005923825,0.0002001263,0.0002295235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005583007,0.00000165358,0.000001662801,0.00003985768,0.000008164203,0.000123929,0.0007441423,0.0006336356,0.2814353,0.000001094778,0.000006020961,0.7169487],"study_design_scores_gemma":[0.0002743119,0.000184765,0.0001051093,0.0005296776,0.00001188827,0.0002274999,0.00004014155,0.5841937,0.4128544,0.00104078,0.0001318869,0.0004058502],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07111364,0.0002778854,0.9258798,0.0006945456,0.0001314895,0.0004021322,5.455207e-7,0.001026739,0.0004732561],"genre_scores_gemma":[0.4821087,0.0000239934,0.5171142,0.0005581725,0.0001413366,1.888667e-8,4.794491e-7,0.00004182042,0.00001134427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7165429,"threshold_uncertainty_score":0.9998079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.016381683787962,"score_gpt":0.2447828910487765,"score_spread":0.2284012072608144,"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."}}