{"id":"W3165204199","doi":"10.48550/arxiv.2105.12633","title":"Edge Detection for Satellite Images without Deep Networks","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Deep learning; Computer science; Satellite; Satellite imagery; Artificial intelligence; Pixel; Training (meteorology); Computer vision; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001031312,0.000284415,0.0002760592,0.0001498306,0.0001201406,0.00009675598,0.0002034635,0.0004648193,0.00002454056],"category_scores_gemma":[0.00001381669,0.0003548603,0.0002135735,0.000232251,0.00003341572,0.0002091033,0.0001227615,0.0005238,0.00001144084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002334631,"about_ca_system_score_gemma":0.0000223182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002553538,"about_ca_topic_score_gemma":0.0000778207,"domain_scores_codex":[0.9989776,0.0000324759,0.0001566276,0.0004980887,0.00003602766,0.0002991756],"domain_scores_gemma":[0.9993021,0.00005185887,0.00008907178,0.000371154,0.0001009531,0.00008483671],"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":[0.00002406346,0.00001866303,0.0004852588,0.0001954993,0.0001414078,0.00003539455,0.00003980749,0.9887859,0.001480226,0.0001041816,0.00007429923,0.008615295],"study_design_scores_gemma":[0.0002904348,0.00001774947,0.001633849,0.000117626,0.0001910842,0.00000713538,0.00009178043,0.9910275,0.004634759,0.0003800259,0.001186318,0.0004217409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2602577,0.0006288552,0.7356288,0.000003469791,0.001530027,0.0002463317,0.000007212131,0.0008380695,0.0008595865],"genre_scores_gemma":[0.9967734,0.001662603,0.0006393743,0.000009742168,0.0002954073,0.00000348507,0.00008475404,0.000064486,0.0004667856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7365157,"threshold_uncertainty_score":0.9998903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933026829014104,"score_gpt":0.1725158683932201,"score_spread":0.1431856001030791,"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."}}