{"id":"W1974391063","doi":"10.1109/jstars.2013.2250922","title":"Multi-Scale Segmentation of Forest Areas and Tree Detection in LiDAR Images by the Attentive Vision Method","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Artificial intelligence; Lidar; Computer science; Segmentation; Computer vision; Image segmentation; Feature (linguistics); Region growing; Scale (ratio); Minimum spanning tree-based segmentation; Object detection; Scale-space segmentation; Tree (set theory); Pattern recognition (psychology); Pixel; Segmentation-based object categorization; Feature extraction; Remote sensing; Object (grammar); Geography; Mathematics; Cartography","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":[0.0004002759,0.0004867922,0.0003481167,0.00139448,0.0002912242,0.0004983995,0.0007599856,0.0004678702,0.0008960979],"category_scores_gemma":[0.0006831455,0.000378234,0.0007898764,0.0005506082,0.0005094369,0.0007161587,0.0005879141,0.0004119418,0.0002437341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003056638,"about_ca_system_score_gemma":0.0003948132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001706195,"about_ca_topic_score_gemma":0.00256134,"domain_scores_codex":[0.9997047,0.00004236238,0.00001325635,0.00008807111,0.0001074643,0.00004410556],"domain_scores_gemma":[0.9997409,0.00009385462,0.00003497532,0.0000337091,0.00007582363,0.00002081985],"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":[0.0001673175,0.0001051032,0.001478525,0.0001966586,0.00006976375,0.0002965337,0.0004573927,0.03147241,0.4190326,0.005234377,0.000764076,0.5407253],"study_design_scores_gemma":[0.0000294421,0.0003142992,0.01046811,0.00003339406,0.0001251206,0.001097971,0.0001802108,0.8454468,0.1329285,0.004461077,0.004844678,0.00007043521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03642912,0.0001810284,0.9619779,0.00003355319,0.00001680464,0.00005695514,0.00001193891,0.0004076809,0.0008850646],"genre_scores_gemma":[0.2756829,0.0002124626,0.7228546,0.00006499696,0.00003736953,0.00007676626,0.00004971087,0.00006914807,0.0009520595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001706195,"threshold_uncertainty_score":0.003392518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328802621868136,"score_gpt":0.2485447134616531,"score_spread":0.2352566872429717,"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."}}