{"id":"W3174867596","doi":"10.3390/rs13132524","title":"Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Computer science; Decoding methods; Segmentation; Encoding (memory); Deep learning; Artificial intelligence; Salient; Pattern recognition (psychology); Telecommunications","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":[0.0006474032,0.001255261,0.000937759,0.001090008,0.0005147351,0.0009522443,0.001730366,0.001476321,0.002659082],"category_scores_gemma":[0.001000881,0.0004810691,0.001017267,0.0009463847,0.0007098331,0.001916714,0.00126864,0.001272443,0.00113478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009972098,"about_ca_system_score_gemma":0.001050633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009054195,"about_ca_topic_score_gemma":0.01422117,"domain_scores_codex":[0.9996915,0.00003613701,0.00001635026,0.0001192933,0.0000646049,0.00007218726],"domain_scores_gemma":[0.9997602,0.0000637172,0.00003042021,0.00004905386,0.00007177211,0.00002486678],"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":[0.0006466093,0.0004375486,0.004953243,0.0001906978,0.0001900531,0.0002183396,0.0001963033,0.1763862,0.02396862,0.005836703,0.01145672,0.7755189],"study_design_scores_gemma":[0.00002116162,0.00007579791,0.0009677935,0.00001708822,0.0000485708,0.00005490586,0.00003327383,0.9810253,0.01263649,0.003195894,0.001908383,0.00001538484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2025086,0.002141393,0.7707869,0.0009308202,0.0002887143,0.0002666661,0.0007495839,0.01374107,0.008586251],"genre_scores_gemma":[0.7291459,0.000714994,0.253451,0.00107005,0.0001601136,0.00019826,0.002862765,0.000425398,0.0119716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009054195,"threshold_uncertainty_score":0.01800299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02252417373187269,"score_gpt":0.2607273118554971,"score_spread":0.2382031381236245,"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."}}