{"id":"W4280613904","doi":"10.1139/geomat-2021-0013","title":"Building detection using a dense attention network from LiDAR and image data","year":2021,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Lidar; Dropout (neural networks); Computer science; Normalization (sociology); Remote sensing; Convolutional neural network; Deep learning; Pooling; Artificial intelligence; Convolution (computer science); Data mining; Pattern recognition (psychology); Artificial neural network; Geography; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001363464,0.00006400967,0.00007487548,0.000007509449,0.0002122936,0.00008248108,0.00008597744,0.00003515921,0.0001503234],"category_scores_gemma":[0.00004953507,0.00006637529,0.00001494818,0.000156997,0.00005908425,0.0001546847,0.0002852282,0.00006042589,0.0001061245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003400178,"about_ca_system_score_gemma":0.000005543318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005301225,"about_ca_topic_score_gemma":0.0001712286,"domain_scores_codex":[0.9992966,0.000046898,0.0001208897,0.000281507,0.0001137007,0.00014041],"domain_scores_gemma":[0.9993834,0.00004874586,0.00004512567,0.0004658368,0.000005980904,0.00005087059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004301618,0.00003147596,0.003912583,0.000008263842,0.00002132775,0.00001615775,0.0001258903,0.0004529728,0.9014379,0.00003032272,0.0004938575,0.09346492],"study_design_scores_gemma":[0.0003226134,0.00001363983,0.2122625,0.00009442644,0.0001594632,0.0001530791,0.0001988172,0.7512926,0.01365575,0.01217138,0.009338784,0.0003369679],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.863562,0.00004197622,0.1354803,0.0001327376,0.0000542457,0.00005760139,0.000007446677,0.00003193204,0.0006317142],"genre_scores_gemma":[0.7776861,0.000007828076,0.2220996,0.00005326128,0.00008477366,4.368164e-7,0.00002568435,0.000007987382,0.00003436837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8877822,"threshold_uncertainty_score":0.2706706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02024562048226382,"score_gpt":0.2574251271991749,"score_spread":0.2371795067169111,"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."}}