{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001452285,0.0004793941,0.0004096825,0.001711542,0.0003126656,0.001512192,0.0005441993,0.0002850479,0.0004249336],"category_scores_gemma":[0.002495081,0.0001731887,0.0002869066,0.001343332,0.0004681667,0.00110452,0.0005550063,0.0003056507,0.000223404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009981946,"about_ca_system_score_gemma":0.001029342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03836696,"about_ca_topic_score_gemma":0.07064059,"domain_scores_codex":[0.9994454,0.0001817541,0.00002102324,0.0001019613,0.0001859341,0.00006391096],"domain_scores_gemma":[0.9989616,0.0003195165,0.0001079427,0.0001624784,0.0003827325,0.00006580559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002783008,0.0003604527,0.08652299,0.0002977434,0.0002104376,0.0001333296,0.0006036407,0.1494896,0.01238414,0.003900426,0.0021691,0.7436498],"study_design_scores_gemma":[0.00002418528,0.0003529262,0.06961547,0.0001041617,0.0001096957,0.000146892,0.001182114,0.9067039,0.01138506,0.00412809,0.006192025,0.00005545879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6752771,0.001106171,0.3114057,0.0004613666,0.00007363946,0.0003236139,0.0005346623,0.001380492,0.009437311],"genre_scores_gemma":[0.8530808,0.0003047133,0.1445367,0.00006094298,0.00002521014,0.00004921114,0.0005946499,0.00004851153,0.001299206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03836696,"threshold_uncertainty_score":0.07628733,"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."}}