{"id":"W4281714090","doi":"10.5194/isprs-archives-xliii-b2-2022-593-2022","title":"ACTIVE REINFORCEMENT LEARNING FOR THE SEMANTIC SEGMENTATION OF IMAGES CAPTURED BY MOBILE SENSORS","year":2022,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Segmentation; Intersection (aeronautics); Machine learning; Reinforcement learning; Task (project management); Set (abstract data type); Metric (unit); Labeled data; Annotation; Artificial neural network; Pattern recognition (psychology)","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":[0.001834287,0.0008634264,0.0008332628,0.0008064677,0.0003650477,0.000667931,0.001293632,0.001258641,0.001267864],"category_scores_gemma":[0.003787447,0.0004321309,0.0005894559,0.0005576424,0.0009346014,0.001171585,0.0007462025,0.001120621,0.0002547636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553442,"about_ca_system_score_gemma":0.0009587768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007067187,"about_ca_topic_score_gemma":0.007230429,"domain_scores_codex":[0.9994829,0.0001754682,0.00001975217,0.0001628482,0.00008469114,0.00007423417],"domain_scores_gemma":[0.9985821,0.0008324902,0.0001812466,0.00009326325,0.0002311944,0.00007965977],"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":[0.0003040442,0.0001871135,0.001575666,0.0000569051,0.00005041137,0.00007353414,0.00006904811,0.8989938,0.005329484,0.002900797,0.0007988289,0.08966032],"study_design_scores_gemma":[0.000003361584,0.00001742515,0.00008562852,0.000001891396,0.00000186638,0.000002971905,0.000002673964,0.9982389,0.0008104618,0.000765811,0.00006730482,0.000001712569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1251051,0.0003989342,0.8708275,0.0004238397,0.00008626129,0.0001189703,0.0001131509,0.001200786,0.001725465],"genre_scores_gemma":[0.9026016,0.00007155687,0.0951352,0.0001487105,0.00002599103,0.00009759652,0.0001531576,0.00005093516,0.001715172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007067187,"threshold_uncertainty_score":0.01405209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384876074066977,"score_gpt":0.262548264248963,"score_spread":0.2486995035082933,"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."}}