{"id":"W4389961031","doi":"10.1109/access.2023.3344797","title":"Remote Sensing Image Road Segmentation Method Integrating CNN-Transformer and UNet","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Upsampling; Feature extraction; Image segmentation; Robustness (evolution); Encoder; Pattern recognition (psychology); Computer vision; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004262006,0.001391886,0.0009686217,0.002659554,0.0004702054,0.00101444,0.001747044,0.0008340855,0.003249406],"category_scores_gemma":[0.0008705839,0.0005862248,0.001293654,0.001775846,0.0003698723,0.001881425,0.001011551,0.0008778065,0.00225905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076543,"about_ca_system_score_gemma":0.001680757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01340963,"about_ca_topic_score_gemma":0.02697467,"domain_scores_codex":[0.9995635,0.00001856377,0.00002419008,0.0001834914,0.0001357445,0.0000745598],"domain_scores_gemma":[0.9997383,0.00002044403,0.00002449436,0.00005878441,0.0001375101,0.00002040301],"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":[0.0002031522,0.000190699,0.003530868,0.0002171592,0.000223085,0.0002021762,0.00008366055,0.08191385,0.04370025,0.004117707,0.01225077,0.8533667],"study_design_scores_gemma":[0.00002373382,0.00007906016,0.002520145,0.00001932489,0.00009585257,0.0003255033,0.0000507563,0.956985,0.02966567,0.003116552,0.007084458,0.00003391012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0449281,0.00060875,0.9327788,0.0002409207,0.0002150004,0.0002801934,0.001158372,0.01093121,0.008858739],"genre_scores_gemma":[0.4602682,0.0007997043,0.5113588,0.0004643972,0.0001720633,0.0002681071,0.008464827,0.0009341581,0.01726965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01340963,"threshold_uncertainty_score":0.02666312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065238652653478,"score_gpt":0.3319488106363291,"score_spread":0.3112964241097943,"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."}}