{"id":"W2993351627","doi":"10.3390/rs11232878","title":"An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Wuhan University; National Natural Science Foundation of China","keywords":"Markov random field; Computer science; Segmentation; Inference; Markov chain; Random field; Artificial intelligence; Maximum a posteriori estimation; A priori and a posteriori; Pattern recognition (psychology); Image segmentation; Mathematics; Machine learning; Statistics","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":[0.001404054,0.0006088066,0.0008240634,0.0008933052,0.0003279684,0.0006794287,0.001416039,0.001047616,0.0007297801],"category_scores_gemma":[0.002329963,0.0004689213,0.001217841,0.0007746196,0.0007238811,0.00125428,0.0006363004,0.00114167,0.0002131955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007282694,"about_ca_system_score_gemma":0.001049604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009870269,"about_ca_topic_score_gemma":0.008552798,"domain_scores_codex":[0.9994823,0.000167123,0.00002648268,0.0001347488,0.000125824,0.00006357464],"domain_scores_gemma":[0.99939,0.000328539,0.0001055613,0.00005569576,0.00008568287,0.00003453515],"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":[0.00009264865,0.00003439012,0.0008535499,0.00006598594,0.00004912866,0.0000832937,0.00005881747,0.9296517,0.005001985,0.01595894,0.0007028828,0.04744667],"study_design_scores_gemma":[0.000001939285,0.000004542695,0.00007435667,0.000001524097,0.000003338039,0.00000877059,0.000001252407,0.9978751,0.000225812,0.001687724,0.0001114268,0.000004136143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01382345,0.0001505411,0.9853012,0.0001148678,0.00001763218,0.00001808029,0.00003832215,0.0001972415,0.0003387194],"genre_scores_gemma":[0.6302347,0.0005312026,0.365097,0.0002251971,0.0001324547,0.0001666831,0.0005486008,0.0002057487,0.00285849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009870269,"threshold_uncertainty_score":0.0196256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063574672496153,"score_gpt":0.2321761487668968,"score_spread":0.2215404020419352,"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."}}