{"id":"W4391468127","doi":"10.1109/jstars.2024.3361444","title":"GANInSAR: Deep Generative Modeling for Large-Scale InSAR Signal Simulation","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Interferometric synthetic aperture radar; Bottleneck; Synthetic aperture radar; Digital elevation model; Artificial intelligence; Metric (unit); Data mining; Ground truth; Pattern recognition (psychology); Remote sensing","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":[0.0009159091,0.001078036,0.0007154892,0.0004582234,0.0002813028,0.0008748765,0.001649932,0.001155046,0.003265243],"category_scores_gemma":[0.002509169,0.0005683147,0.001011128,0.0004508145,0.0006954051,0.000796064,0.001329438,0.00216845,0.00091648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007364861,"about_ca_system_score_gemma":0.0008187231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005276669,"about_ca_topic_score_gemma":0.008414346,"domain_scores_codex":[0.9997343,0.00009272909,0.00001299558,0.00005497728,0.00007790713,0.00002702124],"domain_scores_gemma":[0.9991565,0.0005648068,0.00005558263,0.00009427237,0.00008499978,0.00004373908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002416691,0.00001354889,0.0003086784,0.00002509053,0.00002728317,0.000026816,0.00001297776,0.9831281,0.0007123898,0.004810989,0.001063257,0.00984672],"study_design_scores_gemma":[0.000001383389,0.000002286563,0.00001548181,0.000001355659,0.000001119076,0.000003753533,7.987999e-7,0.998224,0.0001748694,0.00137608,0.0001976057,0.000001304904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006001257,0.0002216997,0.9898456,0.0002023306,0.00004154657,0.0000310487,0.0002494356,0.001998989,0.001408118],"genre_scores_gemma":[0.5527951,0.0007570374,0.436234,0.0005749211,0.0001275527,0.000472313,0.002069882,0.001255295,0.005713827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005276669,"threshold_uncertainty_score":0.01092327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134628294985318,"score_gpt":0.2458301222269049,"score_spread":0.2244838392770517,"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."}}