{"id":"W2920478483","doi":"10.3390/s19051123","title":"A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Satellite Image Processing and Photogrammetry","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"European Commission","keywords":"RANSAC; Scale-invariant feature transform; Artificial intelligence; Remote sensing; Computer science; Ground truth; Mean squared error; Satellite; Computer vision; Point cloud; Digital elevation model; Feature extraction; Ground sample distance; Scale (ratio); Pattern recognition (psychology); Mathematics; Geography; Image (mathematics); Pixel; Engineering; Cartography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002283009,0.0002233762,0.000204484,0.0001625489,0.00005732046,0.0001017842,0.0001012936,0.0001455527,0.00005904943],"category_scores_gemma":[0.00001699258,0.0002272731,0.00009659263,0.0003573922,0.00002816797,0.0001284133,0.000009413446,0.000267581,0.000181657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007545616,"about_ca_system_score_gemma":0.00002095401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005850814,"about_ca_topic_score_gemma":0.000001668088,"domain_scores_codex":[0.9988747,0.00005370829,0.0002292129,0.0002679422,0.0001967676,0.00037767],"domain_scores_gemma":[0.9994268,0.00009223873,0.00004652665,0.0002953414,0.00004390988,0.00009522568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001283822,0.0001422863,0.02447343,0.001741908,0.0001677001,0.00002960177,0.001545843,0.6067271,0.1327283,0.00002292039,0.0001648677,0.2321276],"study_design_scores_gemma":[0.0004249623,0.00002027326,0.001247985,0.00007224141,0.00003605125,0.00003036457,0.0004820226,0.9464761,0.04073963,0.00002183653,0.01004132,0.0004072121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754707,0.001420719,0.01458002,0.00000679099,0.0007122525,0.0002006628,0.000005615505,0.0006143672,0.006988905],"genre_scores_gemma":[0.985182,0.00004504834,0.01415962,0.00005390774,0.00009378594,0.000004441642,0.00002288182,0.00007586149,0.000362494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3397489,"threshold_uncertainty_score":0.9267928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718140037537988,"score_gpt":0.2383179257866002,"score_spread":0.2211365254112203,"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."}}