{"id":"W2995497465","doi":"10.1016/j.rse.2019.111602","title":"A shadow constrained conditional generative adversarial net for SRTM data restoration","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China; Leverhulme Trust","keywords":"Shuttle Radar Topography Mission; Computer science; Remote sensing; Artificial intelligence; Digital elevation model; Generative model; Shadow (psychology); Interpolation (computer graphics); Computer vision; Geology; Image (mathematics); Generative grammar","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":[0.000886539,0.0007749374,0.0009433392,0.0004506093,0.0002951059,0.0006678365,0.001851015,0.001439534,0.005357032],"category_scores_gemma":[0.001535698,0.0005533548,0.0008712586,0.0005304886,0.0005528769,0.0008768423,0.00181623,0.002014012,0.001858789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004722333,"about_ca_system_score_gemma":0.001081321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005664319,"about_ca_topic_score_gemma":0.007660816,"domain_scores_codex":[0.9997055,0.00006295257,0.00001436807,0.00007539683,0.00009696945,0.00004466254],"domain_scores_gemma":[0.9995484,0.0002091084,0.00003331727,0.0000748647,0.0001025702,0.00003172263],"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":[0.0001673516,0.00006769667,0.0002431672,0.00007915237,0.0000750297,0.00009427955,0.00004293093,0.7356448,0.006951493,0.007573176,0.005261177,0.2437997],"study_design_scores_gemma":[0.000002779773,0.000009924947,0.00002229751,0.000003107915,0.000004381772,0.00001294609,0.000001921979,0.9976198,0.0006486322,0.001299302,0.0003715183,0.000003346191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003105316,0.000170355,0.9945948,0.00009663219,0.00006203214,0.00002751085,0.00008792061,0.0009421159,0.0009133082],"genre_scores_gemma":[0.282688,0.0004787137,0.7000924,0.0005926945,0.0001789833,0.0001948798,0.001092859,0.0006101932,0.01407141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005664319,"threshold_uncertainty_score":0.01792103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755807383153162,"score_gpt":0.2292135683280614,"score_spread":0.2116554944965298,"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."}}