{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001067896,0.0006729047,0.0009326701,0.001270957,0.0004765735,0.0004360338,0.00113339,0.0009237063,0.001507218],"category_scores_gemma":[0.001932119,0.0003016395,0.0009554491,0.00100728,0.0005698053,0.001134887,0.0006596806,0.0008470233,0.0002953654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102945,"about_ca_system_score_gemma":0.0009712198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01612194,"about_ca_topic_score_gemma":0.01409255,"domain_scores_codex":[0.9995804,0.0001004425,0.0000187918,0.0001441151,0.00009456428,0.0000615679],"domain_scores_gemma":[0.9992756,0.0003737039,0.00008336791,0.00007716281,0.0001537103,0.00003652289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002974452,0.0001068947,0.001764663,0.00008055717,0.00007006541,0.0001153956,0.00007796402,0.8180073,0.008619386,0.007033747,0.002671588,0.161155],"study_design_scores_gemma":[0.000002199888,0.000007862872,0.0002156129,0.000001608506,0.000005269165,0.00001132905,0.000003488689,0.9976522,0.0007105139,0.001253368,0.0001329946,0.000003471732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05244999,0.0004410105,0.9440388,0.0002161507,0.00004733787,0.00005495532,0.0002643729,0.001358514,0.001128925],"genre_scores_gemma":[0.8104683,0.0002736325,0.1855611,0.0002148507,0.00008612033,0.00008061167,0.00120539,0.0001584095,0.001951651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01612194,"threshold_uncertainty_score":0.03205615,"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."}}