{"id":"W4402261317","doi":"10.1109/igarss53475.2024.10640523","title":"An Enhanced Trans-Involution Network for Building Footprint Extraction from High Resolution Orthoimagery","year":2024,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orthophoto; Footprint; Computer science; Involution (esoterism); High resolution; Artificial intelligence; Computer vision; Geography; Geology; Remote sensing; Paleontology","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":[0.0002684212,0.0009095259,0.0004754322,0.0007989,0.0003177968,0.0004945028,0.001231063,0.0006193973,0.002170209],"category_scores_gemma":[0.0005827867,0.0002705073,0.0005919791,0.0007349303,0.0003686912,0.0009383686,0.0008701153,0.0006673898,0.001045155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005673878,"about_ca_system_score_gemma":0.0005379879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009343687,"about_ca_topic_score_gemma":0.02026845,"domain_scores_codex":[0.9998368,0.00001604195,0.000006492707,0.00006310113,0.00004279991,0.00003480279],"domain_scores_gemma":[0.9998776,0.00002357912,0.00001296956,0.00003015488,0.00004385814,0.0000118602],"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":[0.0003560353,0.0002252922,0.005181595,0.0001128011,0.0001279994,0.0002944176,0.0001123854,0.2016269,0.02816008,0.003977953,0.01142035,0.7484043],"study_design_scores_gemma":[0.000006841729,0.00005207647,0.001003295,0.000008826481,0.00002898719,0.00008846922,0.00002420813,0.9848453,0.0102313,0.001442853,0.002258123,0.000009700828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1493325,0.0008775622,0.8311545,0.0004104715,0.0001590857,0.0001168696,0.001248755,0.00875781,0.00794258],"genre_scores_gemma":[0.7794015,0.0004416713,0.2009363,0.0004083962,0.00006768818,0.0001098759,0.004865355,0.0002999158,0.01346929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009343687,"threshold_uncertainty_score":0.01857859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008254712038787384,"score_gpt":0.2479423198790442,"score_spread":0.2396876078402568,"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."}}