{"id":"W4324092354","doi":"10.3390/electronics12061347","title":"Multi-Attention-Based Semantic Segmentation Network for Land Cover Remote Sensing Images","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"Computer science; Upsampling; Segmentation; Artificial intelligence; Benchmark (surveying); Feature (linguistics); Pattern recognition (psychology); Confusion matrix; Image segmentation; Feature extraction; Key (lock); Land cover; Semantics (computer science); Data mining; Remote sensing; Image (mathematics); Land use; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002500465,0.0001439295,0.0001336277,0.0000889989,0.0001216982,0.0000728696,0.00005674731,0.00007409836,0.00000313735],"category_scores_gemma":[0.00006277318,0.0001638177,0.00006971527,0.0003655932,0.00001790565,0.0000866987,0.000007101174,0.0001309072,0.0001161706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001995392,"about_ca_system_score_gemma":0.00004239066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004730598,"about_ca_topic_score_gemma":0.00002892141,"domain_scores_codex":[0.9990004,0.00002931585,0.0001998052,0.0001980483,0.0001285496,0.0004438876],"domain_scores_gemma":[0.9994915,0.000141805,0.00005010036,0.0002122497,0.00007374283,0.00003061536],"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":[0.00001562035,0.000006834114,0.00017815,0.000138939,0.00005490947,0.000005015792,0.000040019,0.6333644,0.2877077,0.000005142292,0.009673678,0.06880962],"study_design_scores_gemma":[0.0005683928,0.00002205652,0.002054035,0.00004276927,0.00003546341,0.000003274089,0.000006432096,0.9745771,0.01739825,0.0001506506,0.00496839,0.0001732222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07612363,0.0006796099,0.9211605,0.0002576451,0.0004230875,0.0004166227,0.000008441652,0.0008650533,0.00006545526],"genre_scores_gemma":[0.7873249,0.0004773238,0.2088564,0.0001606619,0.0004077577,0.000004303264,0.0009334743,0.000222647,0.001612567],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7123041,"threshold_uncertainty_score":0.6680293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618119171196596,"score_gpt":0.25592705018198,"score_spread":0.2397458584700141,"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."}}