{"id":"W3174951629","doi":"10.5194/isprs-archives-xliii-b3-2021-829-2021","title":"EVALUATION OF SEMI-SUPERVISED LEARNING FOR CNN-BASED CHANGE DETECTION","year":2021,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Change detection; Consistency (knowledge bases); Segmentation; Machine learning; Supervised learning; Pattern recognition (psychology); Image (mathematics); Deep learning; Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002469092,0.0003780302,0.0004169353,0.0008396596,0.0009192172,0.0004841848,0.0009050696,0.0001130662,0.000006915197],"category_scores_gemma":[0.001699733,0.0002721676,0.0003914332,0.001093176,0.001618732,0.0004932269,0.0003369703,0.0003944094,0.000001693452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009453941,"about_ca_system_score_gemma":0.0003071993,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3220302,"about_ca_topic_score_gemma":0.09308461,"domain_scores_codex":[0.9951459,0.0004372147,0.001385055,0.0003605721,0.002240827,0.0004305007],"domain_scores_gemma":[0.9960519,0.001132281,0.001251039,0.0004562042,0.0009952713,0.0001133183],"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":[0.00008094669,0.00001567167,0.0001178801,0.00008676852,0.00009330743,1.145824e-7,0.002007215,0.03212406,0.02273045,0.000002132838,0.00001011916,0.9427313],"study_design_scores_gemma":[0.0009701902,0.0001010725,0.003065708,0.0003783994,0.00010891,0.00005342607,0.001324177,0.9113783,0.07834778,0.002038357,0.001970676,0.0002629877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02686838,0.00006853553,0.9649101,0.001594527,0.001905838,0.0009004854,0.0000655067,0.00007063334,0.003615953],"genre_scores_gemma":[0.9951102,0.0001241119,0.004123344,0.0003636888,0.0001557818,0.000001110827,0.00007169974,0.00001829737,0.00003178443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9682418,"threshold_uncertainty_score":0.9999731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04011460378438498,"score_gpt":0.2738229588430396,"score_spread":0.2337083550586546,"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."}}