{"id":"W4293234904","doi":"10.1109/icgmrs55602.2022.9849283","title":"Building Instance Change Detection from High Spatial Resolution Remote Sensing Images with Improved Instance Segmentation Architecture","year":2022,"lang":"en","type":"article","venue":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Research and Development; Ministry of Natural Resources","keywords":"Change detection; Computer science; Remote sensing; Segmentation; Architecture; Artificial intelligence; Image resolution; Deep learning; Spatial analysis; Convolutional neural network; Image segmentation; Test set; Multispectral image; Data mining; Pattern recognition (psychology); Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000384821,0.0004265018,0.0003628136,0.0005193765,0.000595519,0.0001731948,0.0002080767,0.0001689799,0.00003463315],"category_scores_gemma":[0.0001092346,0.0004593935,0.00006756638,0.0004015439,0.000212424,0.0002440833,0.0001487784,0.00103926,0.000005826616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100211,"about_ca_system_score_gemma":0.0000566044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004028393,"about_ca_topic_score_gemma":0.001151362,"domain_scores_codex":[0.9974411,0.0002737262,0.0004935231,0.000808005,0.00053652,0.0004471359],"domain_scores_gemma":[0.9987662,0.0001324585,0.0003402281,0.0004013905,0.0002738631,0.00008579747],"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":[0.0002241374,0.000008100918,0.00001173215,0.00002686704,0.00009581812,0.00004278285,0.0005175194,0.005353134,0.3634105,0.00009749563,0.00002348608,0.6301885],"study_design_scores_gemma":[0.0009308214,0.0001468246,0.001555016,0.000269513,0.0000335826,0.0002134628,0.0009640596,0.9752189,0.01622437,0.002211519,0.001729102,0.0005028338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3383574,0.0001671525,0.6555321,0.002062929,0.001954939,0.0004155581,0.00004794659,0.0004036828,0.001058371],"genre_scores_gemma":[0.8932807,0.0001920977,0.1054138,0.0002964149,0.0004122054,8.899189e-7,0.0002014859,0.00006908384,0.0001333811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9698657,"threshold_uncertainty_score":0.9997858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02376125865695223,"score_gpt":0.2357931538461733,"score_spread":0.2120318951892211,"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."}}