{"id":"W1975219361","doi":"10.1117/12.760747","title":"Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Change detection; Land cover; Cohen's kappa; Remote sensing; Synthetic aperture radar; Computer science; Contextual image classification; Artificial intelligence; Pattern recognition (psychology); Land use; Geography; Image (mathematics); Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002439839,0.0006170516,0.0006507018,0.001494914,0.0001983651,0.0006962257,0.0003167375,0.0002960584,0.0006813561],"category_scores_gemma":[0.002047604,0.0002413182,0.0005947684,0.0009867722,0.000198603,0.001336232,0.0005364962,0.0002182117,0.0004804902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004653441,"about_ca_system_score_gemma":0.0002975749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002012544,"about_ca_topic_score_gemma":0.003676907,"domain_scores_codex":[0.9989562,0.0002496089,0.00006740709,0.0001407728,0.0005093954,0.00007654055],"domain_scores_gemma":[0.9990528,0.0002475697,0.0001226547,0.0001404349,0.0003863418,0.0000501352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002487291,0.0007073982,0.1110699,0.0006247008,0.0006264022,0.0002472436,0.0002881374,0.09626427,0.2939917,0.0005302839,0.001632492,0.4915303],"study_design_scores_gemma":[0.0002040621,0.002897614,0.3812319,0.00007728677,0.0008002557,0.0006847036,0.0004133457,0.4115681,0.1949104,0.001217325,0.00580714,0.0001879607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515523,0.0008416548,0.04283113,0.0001292094,0.00006879534,0.0001294996,0.0007710035,0.0007105279,0.002965983],"genre_scores_gemma":[0.9445455,0.000314081,0.05344966,0.00005211939,0.0000242379,0.00003254529,0.001074918,0.00003186857,0.000475024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002439839,"threshold_uncertainty_score":0.01290327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02799039608217785,"score_gpt":0.2398755781193398,"score_spread":0.211885182037162,"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."}}