{"id":"W2342699585","doi":"10.1109/jstars.2015.2449296","title":"Road Extraction From Very High Resolution Remote Sensing Optical Images Based on Texture Analysis and Beamlet Transform","year":2015,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Automated Road and Building Extraction","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Panchromatic film; Multispectral image; Computer science; Artificial intelligence; Computation; Computer vision; Edge detection; Enhanced Data Rates for GSM Evolution; Image resolution; Mathematical morphology; Ground truth; Detector; Remote sensing; Image (mathematics); Pattern recognition (psychology); Image processing; Algorithm; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000335945,0.0001807349,0.0003270728,0.0004738985,0.0001149645,0.00009450464,0.00003598257,0.000229986,0.000001269731],"category_scores_gemma":[0.00006534957,0.0001699017,0.00005316997,0.0008386126,0.000037388,0.0001778394,0.000004274235,0.0006080026,3.970768e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001267623,"about_ca_system_score_gemma":0.00006055738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003055882,"about_ca_topic_score_gemma":0.0002764175,"domain_scores_codex":[0.9988063,0.0000394255,0.0004679661,0.0001764846,0.0003072925,0.0002025556],"domain_scores_gemma":[0.9992971,0.0001033086,0.0001451601,0.0001191787,0.0002249642,0.0001103291],"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":[0.0001150714,0.00001660191,0.0000506989,0.00002511258,0.0001596168,0.00003964223,0.0002045603,0.2736039,0.1008971,0.00001019715,0.00008477323,0.6247927],"study_design_scores_gemma":[0.0007284371,0.00005090047,0.04057568,0.0001482427,0.0002301245,0.00004966898,0.00007466853,0.941041,0.01600835,0.0005456412,0.0003657672,0.0001815281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6404521,0.00009249349,0.3585008,0.0003461374,0.0002536252,0.00007703595,0.000004531517,0.00006167129,0.0002116034],"genre_scores_gemma":[0.8313601,0.0001721226,0.1680603,0.00004953051,0.0003075311,1.305386e-8,0.0000207462,0.00001870212,0.00001096099],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6674371,"threshold_uncertainty_score":0.692839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576770677698856,"score_gpt":0.2286140272415171,"score_spread":0.2128463204645286,"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."}}