{"id":"W2753320353","doi":"10.5194/isprs-archives-xlii-2-w6-269-2017","title":"LAND COVER CLASSIFICATION USING A UAV-BORNE SPECTROMETER","year":2017,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Hyperspectral imaging; Remote sensing; Spectrometer; Land cover; Imaging spectrometer; Multispectral image; Environmental science; Calibration; Payload (computing); Footprint; Computer science; Spectral signature; Geography; Optics; Land use; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001646001,0.0002509053,0.0002059169,0.0009684252,0.0002451527,0.0002852333,0.0002315588,0.0002278529,0.00140034],"category_scores_gemma":[0.0001468092,0.00009021429,0.0001938779,0.0005078398,0.00008294521,0.0002870004,0.0001671448,0.0001047238,0.0004187274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002415387,"about_ca_system_score_gemma":0.0001520159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004096325,"about_ca_topic_score_gemma":0.006054158,"domain_scores_codex":[0.9999019,0.0000106064,0.000004262113,0.00003812579,0.00003668484,0.000008390553],"domain_scores_gemma":[0.9998682,0.000016139,0.00001677583,0.00001493023,0.00007150178,0.00001248266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005453685,0.0002040244,0.04769815,0.0001585077,0.00007350527,0.000176703,0.0001571455,0.01022567,0.7579781,0.0003308105,0.001076725,0.1813752],"study_design_scores_gemma":[0.00007817709,0.0008271711,0.276285,0.00005326533,0.0001161364,0.0008186741,0.0005108853,0.2999112,0.4131939,0.0004395178,0.0077027,0.00006327068],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9572208,0.0001961431,0.03635997,0.00003500731,0.00003356013,0.00009751324,0.001430436,0.0009712914,0.003655429],"genre_scores_gemma":[0.926384,0.00007860753,0.07115916,0.00001849359,0.00000653879,0.00002170809,0.0009886923,0.00003058224,0.001312205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004096325,"threshold_uncertainty_score":0.008144975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0253417532512375,"score_gpt":0.2724116099487578,"score_spread":0.2470698566975204,"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."}}