{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004045546,0.0005064544,0.0002740635,0.0007471688,0.0001359593,0.0003834932,0.0004222949,0.0002729089,0.0008772509],"category_scores_gemma":[0.0006125791,0.0001993665,0.0003768857,0.0006313096,0.0001426939,0.000689884,0.0006173098,0.0004381314,0.0002723602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003778968,"about_ca_system_score_gemma":0.000316846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007338959,"about_ca_topic_score_gemma":0.01027085,"domain_scores_codex":[0.9998759,0.00001646844,0.000006354308,0.00003046051,0.00004402086,0.00002679669],"domain_scores_gemma":[0.999808,0.00004569358,0.00002254231,0.0000388754,0.00007369688,0.00001122081],"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":[0.0001993125,0.0001767719,0.006542906,0.00008535453,0.000195849,0.0001707259,0.00007898085,0.425473,0.05797467,0.002278192,0.004179097,0.5026452],"study_design_scores_gemma":[0.000002741212,0.00001149885,0.001415818,0.000003210139,0.00001230715,0.00001648908,0.000007454451,0.9918854,0.005611861,0.0005081437,0.0005211776,0.00000388368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.342561,0.0009146148,0.647792,0.0003060293,0.0001428368,0.00004281112,0.0007294638,0.003120672,0.004390587],"genre_scores_gemma":[0.8886937,0.0001860915,0.1085701,0.00007517212,0.00002870882,0.00001423659,0.0007244892,0.00005888668,0.001648631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007338959,"threshold_uncertainty_score":0.01459253,"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."}}