{"id":"W2157026765","doi":"10.1016/j.isprsjprs.2013.03.006","title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","year":2013,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1478,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Change detection; Computer science; Remote sensing; Pixel; Land cover; Context (archaeology); Earth observation; Object detection; Data mining; Land use; Artificial intelligence; Satellite; Geography; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002105352,0.001537723,0.001840543,0.007335989,0.0003565441,0.002655858,0.001926858,0.001423397,0.001453405],"category_scores_gemma":[0.003623333,0.0009600858,0.001383395,0.005935574,0.00114732,0.002669076,0.001361515,0.001010704,0.001127829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006074501,"about_ca_system_score_gemma":0.0004668481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003555454,"about_ca_topic_score_gemma":0.004984722,"domain_scores_codex":[0.9984002,0.0003396456,0.0001160407,0.0004860829,0.0005499253,0.0001082224],"domain_scores_gemma":[0.9981574,0.0007289061,0.0002411127,0.000357841,0.0004429277,0.00007184892],"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":[0.0001981631,0.0002398309,0.01576965,0.0004715159,0.0004541704,0.0001194015,0.0001551147,0.01409997,0.01735648,0.001699206,0.00166303,0.9477735],"study_design_scores_gemma":[0.00007630213,0.0005816721,0.1329277,0.0003668819,0.001122019,0.001145542,0.0009219769,0.773722,0.03998343,0.03280728,0.01610072,0.0002444137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1116126,0.01027301,0.8679517,0.0005431997,0.0002298186,0.0003531716,0.0009097638,0.002002694,0.006124018],"genre_scores_gemma":[0.5304334,0.008291503,0.4557097,0.0003387831,0.0003822479,0.0002164871,0.001737033,0.0003613626,0.002529478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007335989,"threshold_uncertainty_score":0.01113433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03794654556780053,"score_gpt":0.2246144915920411,"score_spread":0.1866679460242406,"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."}}