{"id":"W4307436727","doi":"10.3390/s22218245","title":"FAPNET: Feature Fusion with Adaptive Patch for Flood-Water Detection and Monitoring","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Segmentation; Multispectral image; Convolutional neural network; Artificial intelligence; Satellite; Deep learning; Remote sensing; Computer vision; Data mining; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005621294,0.001548816,0.0009004059,0.00111266,0.0004098987,0.0005486149,0.001982752,0.001025157,0.003005196],"category_scores_gemma":[0.001386681,0.0003775826,0.000937195,0.00102188,0.0003404693,0.001505816,0.001190939,0.001258419,0.001068899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007861539,"about_ca_system_score_gemma":0.0006381292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01550023,"about_ca_topic_score_gemma":0.01403065,"domain_scores_codex":[0.9997059,0.00003212196,0.000012374,0.0001247828,0.00006489018,0.00005997522],"domain_scores_gemma":[0.9997717,0.00006325119,0.00002411996,0.00004874207,0.00007139847,0.00002083733],"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":[0.001107301,0.0005029412,0.005015583,0.0002882595,0.0003508161,0.000347249,0.00009128891,0.1874715,0.02439719,0.0015955,0.05110161,0.7277307],"study_design_scores_gemma":[0.00003753495,0.0001533655,0.001897055,0.00001687405,0.00004294925,0.00009620244,0.00002901168,0.9806686,0.01147407,0.001704989,0.003860663,0.00001866838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2689554,0.003820567,0.6466685,0.001047935,0.001054854,0.0006066675,0.0128868,0.05688958,0.008069583],"genre_scores_gemma":[0.6976169,0.0006904266,0.2693029,0.0005470393,0.0001695505,0.0003512753,0.02252709,0.0006978447,0.008097016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01550023,"threshold_uncertainty_score":0.03082001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006460682955909413,"score_gpt":0.190517525857813,"score_spread":0.1840568429019036,"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."}}