{"id":"W2981664452","doi":"10.3390/ijgi8110474","title":"GEOBIA Achievements and Spatial Opportunities in the Era of Big Earth Observation Data","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Österreichische Forschungsförderungsgesellschaft; Austrian Science Fund; European Commission","keywords":"Big data; Context (archaeology); Computer science; Data science; Geographic information system; Analytics; Spatial analysis; Earth observation; Spatial contextual awareness; Object (grammar); Inference; Data mining; Remote sensing; Cartography; Geography; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006273629,0.00008098112,0.0001131861,0.0002244776,0.000017398,0.0001166824,0.000421399,0.00004449374,0.00001494472],"category_scores_gemma":[0.0001144245,0.00006661382,0.00002511608,0.00007742629,0.00002463042,0.002751505,0.00004917137,0.0002038755,0.00001192656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004712056,"about_ca_system_score_gemma":0.00004742305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009909224,"about_ca_topic_score_gemma":0.0000316874,"domain_scores_codex":[0.99871,0.00003353541,0.0006211202,0.00004191291,0.0005141706,0.00007921816],"domain_scores_gemma":[0.999,0.0000687584,0.0003698914,0.0001982144,0.0003421892,0.00002096911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001756545,0.00005582566,0.01804128,0.0001371594,0.000183673,0.000007322174,0.004614932,0.0167194,0.005339363,0.00102265,0.001852385,0.9518504],"study_design_scores_gemma":[0.001792368,0.0001158894,0.5223064,0.0003494799,0.00003141659,0.0001187673,0.002057321,0.4330948,0.001818721,0.0003250591,0.03778843,0.0002012973],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.966063,0.00003907819,0.02981153,0.001205261,0.001374672,0.0001614108,0.00005699998,0.0000127648,0.001275283],"genre_scores_gemma":[0.9978949,0.0002220192,0.001332498,0.0001980628,0.0001018828,5.273999e-7,0.0002307478,0.000005094593,0.00001429409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9516491,"threshold_uncertainty_score":0.2716433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05059172061919632,"score_gpt":0.2540544179514229,"score_spread":0.2034626973322265,"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."}}