{"id":"W3043411116","doi":"10.1080/07038992.2020.1791693","title":"Urban Land Cover Mapping from Airborne Hyperspectral Imagery Using a Fast Jointly Sparse Spectral Mixture Analysis Method","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science and Technology Major Project; National Science Foundation","keywords":"Hyperspectral imaging; Remote sensing; Land cover; Cover (algebra); Geography; Environmental science; Cartography; Land use; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004365341,0.0007797705,0.0006095643,0.001284019,0.000321451,0.0005113271,0.0006049011,0.0004542044,0.001344441],"category_scores_gemma":[0.0007986869,0.0004821667,0.001225289,0.0009782312,0.0002901755,0.000993369,0.0008544148,0.0007626042,0.0006767184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002496575,"about_ca_system_score_gemma":0.0006874976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006908515,"about_ca_topic_score_gemma":0.008192927,"domain_scores_codex":[0.9996908,0.0000562452,0.00001405402,0.00006495236,0.0001358707,0.000038162],"domain_scores_gemma":[0.9997222,0.00008983072,0.0000286711,0.00004417832,0.00009763828,0.00001748659],"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":[0.0002308848,0.0001341788,0.001858343,0.0001087609,0.000198042,0.000154739,0.0001546748,0.3576451,0.06367432,0.004368979,0.003454848,0.5680172],"study_design_scores_gemma":[0.000005930875,0.000009021065,0.0003193888,0.000001735628,0.00001135696,0.00003045257,0.000008961933,0.9956357,0.002788797,0.0005700807,0.0006114694,0.000007053191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01567274,0.00009298891,0.9827529,0.00006848999,0.00001685626,0.00002416995,0.00004858988,0.0008096885,0.0005135522],"genre_scores_gemma":[0.2088088,0.0002596753,0.7870731,0.00006851949,0.00005023254,0.0001006254,0.0006823281,0.0002235353,0.002733288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006908515,"threshold_uncertainty_score":0.01373661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02654721244028376,"score_gpt":0.2258732846816322,"score_spread":0.1993260722413485,"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."}}