{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002928096,0.0002270439,0.0002327163,0.00005771318,0.00007521053,0.00004239491,0.00007914289,0.00008502873,0.00006331415],"category_scores_gemma":[0.00001068768,0.0002267549,0.000100227,0.00008248125,0.00006426684,0.00005826643,0.00005234503,0.0002101857,0.00003161889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005538193,"about_ca_system_score_gemma":0.00001135166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000252525,"about_ca_topic_score_gemma":0.000003844019,"domain_scores_codex":[0.9986753,0.00004034356,0.0003281992,0.00030419,0.0004135878,0.0002383351],"domain_scores_gemma":[0.9994538,0.00008203049,0.00004463685,0.000344291,0.00001200772,0.00006325565],"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":[0.000004209453,0.00001710357,0.00001026165,0.00007795078,0.0001431562,0.00001475302,0.0002878225,0.01210137,0.2253623,0.00002791495,0.00006729304,0.7618859],"study_design_scores_gemma":[0.00007884028,0.00001961994,0.0001410592,0.0003246446,0.000102541,0.0000128302,0.0001118267,0.7809374,0.07975522,0.0003536846,0.1379004,0.0002619876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1313858,0.004116721,0.8630262,0.00007484572,0.0001436224,0.0001941955,0.00000442243,0.0002837604,0.0007704108],"genre_scores_gemma":[0.5673766,0.0004538767,0.4320187,0.000007623778,0.00005703691,6.618828e-8,0.0000077496,0.00005200383,0.000026326],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.768836,"threshold_uncertainty_score":0.9246798,"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."}}