{"id":"W4390871472","doi":"10.1109/lgrs.2024.3354175","title":"Spatial-Gated Multilayer Perceptron for Land Use and Land Cover Mapping","year":2024,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Hyperspectral imaging; Convolutional neural network; Artificial intelligence; Land cover; Pattern recognition (psychology); Perceptron; Benchmark (surveying); Deep learning; Feature extraction; Remote sensing; Artificial neural network; Machine learning; Land use; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0001952748,0.0001761471,0.0001528508,0.0001634917,0.0001572763,0.0004874508,0.00004416394,0.00007600251,7.858133e-7],"category_scores_gemma":[0.00005357699,0.0001572276,0.00003588579,0.000174021,0.0001758398,0.0003391613,0.00001432489,0.0001457748,0.000009443616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005164948,"about_ca_system_score_gemma":0.00001260688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004448393,"about_ca_topic_score_gemma":0.00005368387,"domain_scores_codex":[0.998978,0.00002148062,0.0001702256,0.0003746759,0.0001382774,0.0003173144],"domain_scores_gemma":[0.9995484,0.0001519176,0.00002175242,0.0001637062,0.00003440634,0.0000797929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006759834,0.000001624364,0.0002502766,0.0001573873,0.00001711803,0.00003252842,0.000850071,0.002210011,0.8082922,4.130731e-7,0.001177044,0.1870046],"study_design_scores_gemma":[0.0001895434,0.0000114046,0.008238352,0.000231606,0.00002047802,0.00009487934,0.00002064949,0.9782975,0.004063291,0.00001032769,0.008600598,0.0002213545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6433582,0.0000854415,0.3547567,0.0007253508,0.0007138407,0.0001472749,0.000005687017,0.0001900335,0.00001740618],"genre_scores_gemma":[0.9625716,0.0001163193,0.03639808,0.0004894697,0.0001957417,6.995374e-8,0.000007756556,0.00003989998,0.0001810678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9760875,"threshold_uncertainty_score":0.6411557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435751242022765,"score_gpt":0.2314701331248818,"score_spread":0.2071126207046541,"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."}}