{"id":"W4390339912","doi":"10.21203/rs.3.rs-3787852/v1","title":"PMNet: A Multi-branch and Multi-scale Semantic Segmentation Approach to Water Extraction from high-resolution remote sensing images with Edge-Cloud Computing","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Key Research and Development Program of China; Government of Jiangsu Province; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Cloud computing; Segmentation; Convolutional neural network; Enhanced Data Rates for GSM Evolution; Remote sensing application; Remote sensing; Image segmentation; Image resolution; Computer vision; Hyperspectral imaging","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":[0.0003813764,0.0008674596,0.0007211737,0.000994466,0.0004766658,0.0007117613,0.001427041,0.0007913694,0.001459618],"category_scores_gemma":[0.0006192279,0.000427389,0.0008000683,0.001168808,0.0003656386,0.001697848,0.00107885,0.0009200309,0.0003722133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007305754,"about_ca_system_score_gemma":0.0009732516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01146487,"about_ca_topic_score_gemma":0.01389545,"domain_scores_codex":[0.9997936,0.00002793904,0.00001026439,0.00006774948,0.00005653979,0.00004402257],"domain_scores_gemma":[0.9998496,0.00003264436,0.00002040318,0.00002075276,0.00005764195,0.00001900879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003563598,0.0002855081,0.004660827,0.0001129715,0.0001886262,0.0002922229,0.0001493251,0.5584114,0.01909827,0.005390339,0.006418393,0.4046358],"study_design_scores_gemma":[0.000003368907,0.00001050462,0.0002229789,0.000002142063,0.000006277623,0.000009919385,0.000009743143,0.9969818,0.001248353,0.001198656,0.0003033348,0.000002943857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06286156,0.0003830991,0.9315544,0.0002893842,0.00007240035,0.00007952563,0.00025445,0.002278597,0.002226733],"genre_scores_gemma":[0.6909851,0.0003822164,0.301317,0.0002988793,0.0000841827,0.0001175643,0.001125463,0.0002333032,0.00545623],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01146487,"threshold_uncertainty_score":0.02279627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05653906668258447,"score_gpt":0.3495054071537116,"score_spread":0.2929663404711272,"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."}}