{"id":"W4388233367","doi":"10.3390/ijgi12110450","title":"Enhancing Crop Classification Accuracy through Synthetic SAR-Optical Data Generation Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada; Deutsches Zentrum für Luft- und Raumfahrt; University of Ottawa; National Aeronautics and Space Administration","keywords":"Synthetic aperture radar; Artificial intelligence; Computer science; Deep learning; Remote sensing; Machine learning; Pattern recognition (psychology); Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006730501,0.0001244616,0.0001421559,0.00006923625,0.0002479856,0.000382294,0.0006246105,0.0001024383,0.0001341622],"category_scores_gemma":[0.0008467564,0.00005650501,0.0000892212,0.0003258608,0.00002816471,0.005235457,0.0001490626,0.0002459642,0.0001900025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021397,"about_ca_system_score_gemma":0.00002773043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005458717,"about_ca_topic_score_gemma":0.00004653142,"domain_scores_codex":[0.9982182,0.00006536881,0.0007028601,0.0001212423,0.0007044199,0.0001879395],"domain_scores_gemma":[0.9982988,0.0002133718,0.0006626976,0.00007352448,0.0006909557,0.0000606467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001083415,0.00007484943,0.001935826,0.00001443382,0.0001488392,0.00001419567,0.00156634,0.006279238,0.5514478,0.001583374,0.004351761,0.432475],"study_design_scores_gemma":[0.001277689,0.000477181,0.1232136,0.0004001693,0.000197471,0.0007440879,0.009188914,0.3412591,0.05643032,0.0008709633,0.465011,0.0009294671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837434,0.00004930576,0.0106121,0.003311807,0.001557654,0.000118681,0.00002961709,0.00006098084,0.0005164586],"genre_scores_gemma":[0.9953151,0.0002091373,0.001446825,0.0003368678,0.001559768,0.000001459625,0.001102403,0.000001162532,0.00002723886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4950175,"threshold_uncertainty_score":0.379558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06825055886443646,"score_gpt":0.3058461131561867,"score_spread":0.2375955542917502,"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."}}