{"id":"W3205619972","doi":"10.1109/igarss47720.2021.9553172","title":"Building Change Detection in Off-Nadir Images Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Nadir; Deep learning; Ground truth; Change detection; Computer science; Footprint; Remote sensing; Satellite; Artificial intelligence; Tracking (education); Satellite imagery; Computer vision; Geology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001246492,0.0001057268,0.000113054,0.0001518595,0.00005421015,0.00006741751,0.00004006659,0.00007228352,0.00002735244],"category_scores_gemma":[0.0001176396,0.0001266468,0.00003125097,0.0004401806,0.00001314753,0.0002774558,0.00002017024,0.0002145725,0.00001869836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002171903,"about_ca_system_score_gemma":0.000007304605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007752288,"about_ca_topic_score_gemma":0.0002089804,"domain_scores_codex":[0.9993067,0.00004482138,0.0001646588,0.0001776202,0.00009327902,0.0002129509],"domain_scores_gemma":[0.9997064,0.00003981084,0.00002266453,0.0001430087,0.00005137541,0.00003673292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.844887e-7,0.000003770356,0.0003278549,0.00002284356,0.000004012567,0.00001996436,0.0001491682,0.01412767,0.6802391,0.000009235188,0.000002199409,0.3050933],"study_design_scores_gemma":[0.00009237247,0.000003215597,0.008604388,0.00003502177,0.00000507202,0.00003446208,0.0001170087,0.6242768,0.3656649,0.00002433485,0.001025174,0.000117289],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6287465,0.0006436792,0.3682172,0.00003352232,0.0002872251,0.00007543343,2.336737e-7,0.0003596265,0.001636578],"genre_scores_gemma":[0.9633249,0.00009877219,0.03629075,0.00001857571,0.000122031,0.000002475961,0.0000025387,0.00003923788,0.000100712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6101491,"threshold_uncertainty_score":0.5164505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03239892195790438,"score_gpt":0.254495889791729,"score_spread":0.2220969678338246,"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."}}