{"id":"W2038552727","doi":"10.1109/igarss.2007.4423372","title":"Change detection of buildings in urban environment from high spatial resolution satellite images using existing cartographic data and prior knowledge","year":2007,"lang":"en","type":"article","venue":"","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Change detection; Computer science; Process (computing); Remote sensing; Image resolution; Object detection; Spatial analysis; Computer vision; Geography; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000417783,0.0001308153,0.0001502614,0.0002445146,0.00004327041,0.00002292365,0.00009039424,0.00009165707,0.000003341935],"category_scores_gemma":[0.00004201387,0.0001464689,0.00001573342,0.0002000624,0.00006542289,0.0002697287,0.00007964562,0.0001198048,0.000002109145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00013145,"about_ca_system_score_gemma":0.000003875367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774749,"about_ca_topic_score_gemma":0.001014142,"domain_scores_codex":[0.9990502,0.0000304279,0.0003228237,0.0002883674,0.0001119054,0.0001962868],"domain_scores_gemma":[0.9994228,0.00007980687,0.0000729228,0.0003637665,0.00001789869,0.00004278603],"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":[0.00001175093,0.00001447313,0.004803407,0.00003806794,0.000008453047,0.000002465597,0.0004713016,0.00009648538,0.7546443,0.000004417654,0.000001354439,0.2399035],"study_design_scores_gemma":[0.0003204919,0.00001892553,0.4498802,0.0001137073,0.00003192331,0.000004676948,0.00009758641,0.3286582,0.2198389,0.00003016217,0.000807256,0.0001979814],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7498434,0.001427182,0.2482292,0.000006035021,0.0001260672,0.0001672694,0.00001167198,0.00007142153,0.000117783],"genre_scores_gemma":[0.9674099,0.0002684811,0.03208435,0.00000206997,0.0001591999,9.73298e-7,0.00004185185,0.00002764637,0.000005550429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5348054,"threshold_uncertainty_score":0.5972829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05827173070775962,"score_gpt":0.2619838098343505,"score_spread":0.2037120791265909,"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."}}