{"id":"W3005621308","doi":"10.3390/s20040993","title":"Affiliated Fusion Conditional Random Field for Urban UAV Image Semantic Segmentation","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Aeronautical Science Foundation of China; National Natural Science Foundation of China","keywords":"Conditional random field; Computer science; Artificial intelligence; Segmentation; Computer vision; Aerial image; Field (mathematics); Terrain; Image segmentation; Object (grammar); Scale (ratio); Image (mathematics); Pattern recognition (psychology); Geography; Cartography; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004540914,0.00009363305,0.0001070751,0.00002683334,0.0001449708,0.00004291141,0.0002211633,0.00003426336,0.00002842071],"category_scores_gemma":[0.00007757705,0.00009176553,0.00005871856,0.0002571918,0.000021956,0.0001935483,0.00005649085,0.00007145846,0.00007650557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000150308,"about_ca_system_score_gemma":0.00001449038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001761984,"about_ca_topic_score_gemma":0.000001365971,"domain_scores_codex":[0.9992114,0.00003065417,0.0001659905,0.0002948547,0.0001363772,0.0001607163],"domain_scores_gemma":[0.9992808,0.0003160324,0.00007767112,0.000172239,0.00006901284,0.00008422362],"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":[0.0006516773,0.000237567,0.0005413318,0.0001784022,0.0001047751,0.00004449662,0.004221879,0.052625,0.6277284,0.07279845,0.2107853,0.03008279],"study_design_scores_gemma":[0.002325012,0.0001680296,0.0003430714,0.0000111226,0.00001600713,0.0000074125,0.00006186597,0.888208,0.09544911,0.006413435,0.006747846,0.0002490615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05966809,0.00001812107,0.9275799,0.01160031,0.00009698883,0.0005844735,0.00001670537,0.0002259827,0.0002094264],"genre_scores_gemma":[0.8981007,0.00001192978,0.0981083,0.003166963,0.0002112567,0.00008390422,0.000112628,0.00001287841,0.0001914742],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8384326,"threshold_uncertainty_score":0.374209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01797600338495906,"score_gpt":0.2700800010348486,"score_spread":0.2521039976498895,"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."}}