{"id":"W3048021468","doi":"10.5194/isprs-archives-xliii-b1-2020-85-2020","title":"SPATIAL RESOLUTION ENHANCEMENT OF LAND COVER MAPPING USING DEEP CONVOLUTIONAL NETS","year":2020,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Land cover; Remote sensing; Ground truth; Multispectral image; Satellite; Image resolution; Convolutional neural network; Satellite imagery; Scale (ratio); Computer science; Earth observation; Sensor fusion; Environmental science; Land use; Cartography; Artificial intelligence; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009858041,0.0004278558,0.0004776072,0.000707455,0.000846167,0.0004454641,0.00119133,0.000109806,0.00001108943],"category_scores_gemma":[0.0006838074,0.0003051726,0.000337454,0.000944032,0.002473086,0.0005227036,0.0006254576,0.0004379038,0.000004917276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008628554,"about_ca_system_score_gemma":0.0002172812,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4527772,"about_ca_topic_score_gemma":0.03982176,"domain_scores_codex":[0.9953865,0.0002513222,0.001661507,0.0003819521,0.001824213,0.0004945272],"domain_scores_gemma":[0.9968917,0.0006777236,0.001485591,0.0004048284,0.0003685525,0.000171571],"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":[0.0001377402,0.00001497233,0.0002748124,0.00008396783,0.0001129858,2.835447e-7,0.003032677,0.04389734,0.02240136,0.000004880932,0.00004243834,0.9299966],"study_design_scores_gemma":[0.0007634147,0.0001072185,0.002887827,0.000366003,0.00004956933,0.0000763843,0.0007968647,0.969489,0.02053277,0.001218351,0.003400236,0.0003123709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0250854,0.00005564071,0.9661177,0.002452771,0.001620844,0.0006346383,0.00009696846,0.00006312849,0.003872943],"genre_scores_gemma":[0.9910326,0.0001542928,0.007819951,0.0007071023,0.0001823927,2.541877e-7,0.00006258223,0.00001675569,0.00002406089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9659472,"threshold_uncertainty_score":0.99994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02649047411484274,"score_gpt":0.2453152837298235,"score_spread":0.2188248096149808,"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."}}