{"id":"W4408254235","doi":"10.1109/access.2025.3548262","title":"AED-Net: A High-Resolution Remote Sensing Image Road Extraction Method Integrating Atrous Spatial Pyramid Pooling and Efficient Channel Attention Mechanism","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pooling; Computer science; Pyramid (geometry); Artificial intelligence; Computer vision; Channel (broadcasting); Image resolution; Pattern recognition (psychology); Remote sensing; Computer network; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004417287,0.000254144,0.0002410745,0.0003473511,0.0003006298,0.000302141,0.0001164921,0.0002111465,0.000004597742],"category_scores_gemma":[0.00005434881,0.0002622689,0.00007258265,0.0003403589,0.00002281288,0.0004789798,0.00004836237,0.0003867217,0.000006681144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002400645,"about_ca_system_score_gemma":0.00001777646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002887463,"about_ca_topic_score_gemma":0.000191486,"domain_scores_codex":[0.9986298,0.00008250606,0.0003761366,0.0003749911,0.000198932,0.000337653],"domain_scores_gemma":[0.9994626,0.00006807718,0.0001324665,0.00018911,0.00009390037,0.00005384604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002997918,0.00001733644,0.000004115042,0.0001541285,0.00005899752,0.00001596235,0.0001457038,0.1600613,0.5849875,0.0001129553,0.00008553269,0.2543265],"study_design_scores_gemma":[0.0003475428,0.00001732424,0.001409293,0.0003115904,0.00008248463,0.00004401273,0.00008815019,0.8964165,0.09995928,0.001075495,0.00002625292,0.0002220963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4059761,0.00008718858,0.5910784,0.00006529491,0.0019064,0.0001712403,0.000003102418,0.0005420026,0.0001702976],"genre_scores_gemma":[0.9762066,0.00004729034,0.02330483,0.00002698838,0.0003119066,0.000002979292,0.00001867188,0.00004209949,0.00003865462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7363552,"threshold_uncertainty_score":0.999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00973406270695353,"score_gpt":0.2892426424648322,"score_spread":0.2795085797578787,"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."}}