{"id":"W4405660110","doi":"10.1016/j.rse.2024.114577","title":"Ground surface displacement measurement from SAR imagery using deep learning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defense Acquisition Program Administration; Institute of Civil-Military Technology Cooperation; Ministry of Trade, Industry and Energy; Southern Methodist University; Nvidia","keywords":"Remote sensing; Geology; Synthetic aperture radar; Displacement (psychology); Surface (topology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000159794,0.0005282917,0.000209982,0.0004009261,0.0001092345,0.0002791601,0.0004117829,0.0003155425,0.0009317518],"category_scores_gemma":[0.0004964517,0.0001800283,0.0002493304,0.0004814948,0.0001672745,0.0005426128,0.000377941,0.000480907,0.000366422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003147331,"about_ca_system_score_gemma":0.0003905845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006008213,"about_ca_topic_score_gemma":0.01129952,"domain_scores_codex":[0.9999086,0.000009882717,0.000004633739,0.00003073088,0.00003160124,0.00001454763],"domain_scores_gemma":[0.9998989,0.0000198567,0.00002057872,0.00001636492,0.00003583964,0.000008388704],"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":[0.0001307119,0.0001432566,0.01682387,0.00009281562,0.00009365132,0.0001351518,0.00006146455,0.609809,0.04686888,0.001377726,0.002990083,0.3214734],"study_design_scores_gemma":[0.000003274167,0.00001905432,0.003589617,0.000004076733,0.000005644761,0.00001362975,0.000009829561,0.9899995,0.005496457,0.0004486929,0.0004034053,0.000006933725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5095389,0.0005222536,0.4790759,0.0003976148,0.0001352144,0.00005355884,0.00106884,0.003556673,0.005650963],"genre_scores_gemma":[0.942767,0.0001337348,0.05470778,0.00006008797,0.00001754158,0.00001709808,0.0008072795,0.00003279919,0.001456653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006008213,"threshold_uncertainty_score":0.0119465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535123957280114,"score_gpt":0.2133520055957789,"score_spread":0.1980007660229778,"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."}}