{"id":"W4403827183","doi":"10.1109/tgrs.2024.3486787","title":"A Multitask CNN-Transformer Network for Semantic Change Detection From Bitemporal Remote Sensing Images","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Change detection; Remote sensing; Transformer; Artificial intelligence; Geology","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":[0.0003627835,0.001095315,0.0004990349,0.0006503065,0.0003062343,0.0005310947,0.001563041,0.0007990417,0.003304394],"category_scores_gemma":[0.0008182937,0.0003318065,0.0006966894,0.0005606106,0.0003054275,0.001196414,0.0008727201,0.0008123523,0.001114852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001041125,"about_ca_system_score_gemma":0.0009474642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01634025,"about_ca_topic_score_gemma":0.02911729,"domain_scores_codex":[0.9998139,0.00001873724,0.000008041758,0.0000800721,0.00004147258,0.00003785416],"domain_scores_gemma":[0.9998571,0.00003159389,0.00001485559,0.00002998046,0.00004788251,0.0000185583],"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":[0.0004195093,0.0003175391,0.003914628,0.0002194987,0.0002657426,0.000342767,0.00009633699,0.2027245,0.04130746,0.005154031,0.02220136,0.7230367],"study_design_scores_gemma":[0.00001132777,0.00006116316,0.0007628161,0.000007435553,0.00002619217,0.00005330368,0.00001791938,0.9878041,0.00698543,0.002174392,0.002084992,0.00001101913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1303725,0.001369667,0.8422878,0.0008557338,0.0003495113,0.0002606377,0.002786216,0.01239553,0.009322385],"genre_scores_gemma":[0.7038868,0.0006462653,0.2696197,0.0005450485,0.0001392234,0.0002247234,0.00808246,0.0003436512,0.01651218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01634025,"threshold_uncertainty_score":0.03249031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688794348173996,"score_gpt":0.2516254297116924,"score_spread":0.2247374862299525,"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."}}