{"id":"W2546821789","doi":"10.1109/igarss.2016.7729408","title":"Road network extraction via deep learning and line integral convolution","year":2016,"lang":"en","type":"article","venue":"","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convolution (computer science); Computer science; Convolutional neural network; Artificial intelligence; Pixel; Context (archaeology); Line (geometry); Deep learning; Feature extraction; Pattern recognition (psychology); Artificial neural network; Computer vision; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001029219,0.00009324903,0.00008097997,0.00004115331,0.00007274705,0.00001834693,0.00002213296,0.00009477011,0.000156735],"category_scores_gemma":[0.00001659227,0.00006377593,0.00002146807,0.00007397329,0.00001690182,0.0002586775,0.000008610677,0.0001304732,0.00006650344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005428818,"about_ca_system_score_gemma":0.000001973636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002244895,"about_ca_topic_score_gemma":0.00002253517,"domain_scores_codex":[0.9995211,0.00001573782,0.0001238765,0.00010736,0.00006077899,0.0001711652],"domain_scores_gemma":[0.9998299,0.00003645279,0.0000250714,0.00004709893,0.00001905706,0.00004237091],"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":[0.00001236407,0.00000833562,0.002312123,0.00001557953,0.0000252558,0.000002311514,0.00002706515,0.01426489,0.1269209,0.0002756031,0.0005947021,0.8555408],"study_design_scores_gemma":[0.0005375747,0.00009252457,0.03067812,0.0001110435,0.00002925728,0.00009777017,0.0000508518,0.926415,0.01001855,0.0005554034,0.03107905,0.0003348823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3034243,0.0004958708,0.6911319,0.00007244309,0.000658908,0.00006288195,2.529673e-7,0.001276806,0.00287664],"genre_scores_gemma":[0.9967371,0.0003723785,0.001686379,0.0000079323,0.0003016281,0.00000454174,0.000003424525,0.0000182799,0.0008683971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9121501,"threshold_uncertainty_score":0.2600707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005274731105027161,"score_gpt":0.2208312837848982,"score_spread":0.2155565526798711,"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."}}