{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005499644,0.001152131,0.0007606336,0.001961178,0.0005356023,0.0006399503,0.001539926,0.0009377518,0.001559707],"category_scores_gemma":[0.0009932143,0.0003719632,0.001096051,0.001250186,0.0005489795,0.001936765,0.0008427784,0.0008954083,0.0004951809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001880089,"about_ca_system_score_gemma":0.00097365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02396902,"about_ca_topic_score_gemma":0.02529204,"domain_scores_codex":[0.9996579,0.00004039344,0.00001617215,0.0001600855,0.00006419334,0.00006119643],"domain_scores_gemma":[0.999755,0.00006318388,0.00003357428,0.00003434593,0.00009600483,0.00001782132],"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":[0.0005192378,0.0003203741,0.005752306,0.0001622384,0.0001888246,0.0002870168,0.0002405028,0.4204182,0.01895213,0.00876554,0.008966766,0.5354269],"study_design_scores_gemma":[0.000004772093,0.00003095304,0.0007502986,0.00000643569,0.00002967923,0.00003719263,0.00001769549,0.9928153,0.002828668,0.002626889,0.000845895,0.00000621665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.15121,0.001852343,0.8312888,0.0008512322,0.0002060216,0.0002156053,0.001409231,0.005694078,0.007272669],"genre_scores_gemma":[0.8836944,0.0005623325,0.1042085,0.0003753554,0.0001455745,0.0001450028,0.00288236,0.0001844675,0.007801885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02396902,"threshold_uncertainty_score":0.04765904,"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."}}