{"id":"W2902463896","doi":"10.3390/ijgi7120462","title":"Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops","year":2018,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Volunteered geographic information; Remote sensing; Classifier (UML); Computer science; Contextual image classification; Vegetation (pathology); Artificial intelligence; Object based; Cartography; Geography; Pattern recognition (psychology); Object (grammar); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005686582,0.0001708674,0.0001544696,0.0003700453,0.0001730229,0.0003804746,0.000304205,0.0001049029,0.0003118776],"category_scores_gemma":[0.0004586339,0.0001378462,0.00007528719,0.0002328206,0.00009348639,0.006375271,0.0001522455,0.0003383027,0.0006193426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002643533,"about_ca_system_score_gemma":0.00001982969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004489291,"about_ca_topic_score_gemma":0.0001916859,"domain_scores_codex":[0.9979393,0.00005264086,0.0007905728,0.00008356219,0.000943463,0.0001904506],"domain_scores_gemma":[0.9983735,0.00004632044,0.0008343723,0.00008635211,0.0005299507,0.0001295184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006687094,0.00009137438,0.1683252,0.00006665135,0.0002915856,0.00001069824,0.02325368,0.01366363,0.006242054,0.001062386,0.02599963,0.7603244],"study_design_scores_gemma":[0.001477234,0.0005992239,0.3786555,0.0002537002,0.0000433466,0.0003682032,0.0008766102,0.05956145,0.00142251,0.000659788,0.5556707,0.0004117244],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392539,0.00004206411,0.04667314,0.002178292,0.00252028,0.0003390924,0.00002119994,0.00006308732,0.008908893],"genre_scores_gemma":[0.9894553,0.00003318579,0.008768224,0.001276599,0.000258361,0.000001261565,0.0001044704,0.000006031039,0.00009651321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7599127,"threshold_uncertainty_score":0.7960603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002198269200002364,"score_gpt":0.2145861870750222,"score_spread":0.2123879178750199,"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."}}