{"id":"W2474355323","doi":"10.5194/isprsarchives-xl-1-w5-263-2015","title":"A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER","year":2015,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Segmentation; Computer science; Image segmentation; Scale (ratio); Computer vision; Scale-space segmentation; Segmentation-based object categorization; Scale space; Pixel; Pattern recognition (psychology); Minimum spanning tree-based segmentation; Land cover; Classifier (UML); Object (grammar); Range segmentation; Random forest; Image (mathematics); Image processing; Geography; Cartography; Land use","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":[0.001928172,0.001205084,0.001788499,0.004030627,0.0007294624,0.001615034,0.002449563,0.001649011,0.002188436],"category_scores_gemma":[0.00183212,0.000624326,0.002168409,0.002884992,0.0006931581,0.001768481,0.001080172,0.001373808,0.00155156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014084,"about_ca_system_score_gemma":0.001097111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009569635,"about_ca_topic_score_gemma":0.009512573,"domain_scores_codex":[0.9983639,0.0002651871,0.00009554093,0.0004718561,0.0006603896,0.0001431261],"domain_scores_gemma":[0.9993063,0.0001729205,0.00007520374,0.00009711504,0.0003110394,0.00003745647],"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":[0.0001008398,0.0001756477,0.001845377,0.0002425312,0.0002616225,0.0001448981,0.000103588,0.1295402,0.02113805,0.01977117,0.007585282,0.8190907],"study_design_scores_gemma":[0.000008386425,0.00003299156,0.0006916977,0.00001863031,0.00003490513,0.00007624382,0.00001515807,0.9843087,0.003122021,0.007639398,0.004030814,0.00002113871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007557519,0.0001373632,0.9978654,0.00003011923,0.00001827829,0.00003971895,0.00005292763,0.0008919864,0.0002085529],"genre_scores_gemma":[0.04789091,0.0002954191,0.9491801,0.00009636411,0.000100085,0.0002319174,0.0005876909,0.0002718379,0.001345601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009569635,"threshold_uncertainty_score":0.01902789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01986114917238836,"score_gpt":0.2649953569412115,"score_spread":0.2451342077688232,"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."}}