{"id":"W4379525877","doi":"10.1109/iccect57938.2023.10141307","title":"Satellite Image Parcel Segmentation and Extraction Based on U-Net Convolution Neural Network Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Image segmentation; Convolutional neural network; Satellite; Artificial neural network; Computer vision; Scale-space segmentation; Pattern recognition (psychology); Convolution (computer science); Satellite imagery; Remote sensing; Geography; Engineering","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":[0.0003391241,0.0006403484,0.0005872717,0.0007780271,0.0003595318,0.000733395,0.0008895065,0.0006996815,0.001428545],"category_scores_gemma":[0.0006413981,0.0003114592,0.0007289859,0.000811775,0.0003471012,0.001110367,0.0004034019,0.0004863676,0.0003299391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008659158,"about_ca_system_score_gemma":0.0008430772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02238043,"about_ca_topic_score_gemma":0.01446779,"domain_scores_codex":[0.9998139,0.00002047547,0.00001160042,0.00006607432,0.000052407,0.00003560616],"domain_scores_gemma":[0.9998541,0.0000360796,0.00002072003,0.00001527155,0.00006418807,0.000009608749],"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":[0.0002137946,0.0000838275,0.003426958,0.00009931833,0.0000845505,0.0001689536,0.00008841612,0.7264526,0.01369731,0.005068393,0.002159795,0.2484562],"study_design_scores_gemma":[0.000001251673,0.000007687088,0.0002369223,0.000001923204,0.000005631454,0.00001314476,0.000003009687,0.9979119,0.001280417,0.0003702473,0.0001648966,0.000003061742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06702124,0.000475415,0.9268071,0.0001955402,0.00006595131,0.00007487281,0.0002119189,0.001623027,0.003524914],"genre_scores_gemma":[0.8194387,0.0006328215,0.1714887,0.0001470766,0.00005011234,0.0001515622,0.0006181799,0.00008663022,0.007386262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02238043,"threshold_uncertainty_score":0.04450035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137423824438536,"score_gpt":0.2484339299912433,"score_spread":0.2346915475473897,"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."}}