{"id":"W2040556018","doi":"10.5194/isprsarchives-xxxviii-5-w12-43-2011","title":"PIXEL VS OBJECT-BASED IMAGE CLASSIFICATION TECHNIQUES FOR LIDAR INTENSITY DATA","year":2012,"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 and LiDAR Applications","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Remote sensing; Ranging; Land cover; Computer science; Segmentation; Terrain; Pixel; Artificial intelligence; Contextual image classification; Pattern recognition (psychology); Computer vision; Geography; Land use; Cartography; Image (mathematics); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002284402,0.0004363835,0.000428934,0.0005629341,0.00169021,0.0006547914,0.002331323,0.0001173944,0.00001292831],"category_scores_gemma":[0.001010837,0.000285174,0.000331605,0.0009334154,0.004014282,0.0008343447,0.00144164,0.0004278298,0.000009219532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008452136,"about_ca_system_score_gemma":0.0001711266,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6636499,"about_ca_topic_score_gemma":0.1023502,"domain_scores_codex":[0.9955561,0.0002666863,0.001363487,0.0005128587,0.001669067,0.0006317983],"domain_scores_gemma":[0.9958517,0.001095719,0.001663428,0.0009536145,0.0002306429,0.0002048974],"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":[0.0001800646,0.00003788547,0.0005686032,0.00003115435,0.00005068668,6.441374e-8,0.001723519,0.0004166728,0.01094319,0.000006885203,0.0002514877,0.9857898],"study_design_scores_gemma":[0.0005009202,0.0001113925,0.008805856,0.0001791664,0.00006213042,0.0000766633,0.001229523,0.9454506,0.01878709,0.002464616,0.02200748,0.0003245401],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008142813,0.00001715235,0.9723101,0.006274639,0.001271886,0.001055618,0.0002044837,0.00007761799,0.01064575],"genre_scores_gemma":[0.9732258,0.00006847142,0.02465166,0.001611704,0.0001999737,7.525104e-7,0.0001382115,0.00001631724,0.00008716321],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9854652,"threshold_uncertainty_score":0.9999601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02931447688426528,"score_gpt":0.2815127172227122,"score_spread":0.2521982403384469,"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."}}