{"id":"W2979209462","doi":"10.1117/12.2527767","title":"Mapping of damaged buildings through simulation and change detection of shadows using LiDAR and multispectral data","year":2019,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Multispectral image; Shadow (psychology); Lidar; Computer science; Remote sensing; Event (particle physics); Image resolution; High resolution; Data set; Change detection; Environmental science; Geology; Computer vision; Artificial intelligence","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":[0.000332104,0.0004241881,0.0003625278,0.0005676061,0.0002700537,0.0005409951,0.0006037117,0.0007832529,0.001007291],"category_scores_gemma":[0.0006379627,0.0003035215,0.000473297,0.0003848587,0.0003497495,0.0004775074,0.0004451026,0.0003056737,0.0001268971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004563307,"about_ca_system_score_gemma":0.0004757001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122741,"about_ca_topic_score_gemma":0.01187645,"domain_scores_codex":[0.9998913,0.00002457931,0.00000774743,0.00002171482,0.00003081778,0.00002384026],"domain_scores_gemma":[0.9996976,0.0001436036,0.00003894398,0.00004393191,0.00004365938,0.00003231983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002156849,0.0000388871,0.001850028,0.00001400152,0.000009364017,0.00004849566,0.00003190058,0.9928087,0.001516894,0.0002076032,0.00004598497,0.003406611],"study_design_scores_gemma":[0.000002420775,0.0000111717,0.0003829164,9.100149e-7,0.000001849112,0.00000610174,0.00001077782,0.9990988,0.000364338,0.0000710064,0.00004754898,0.00000216521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8896101,0.0001111121,0.1064958,0.0001142107,0.00002991171,0.00007694733,0.0003090707,0.0006495884,0.002603331],"genre_scores_gemma":[0.9790924,0.00005102146,0.02024743,0.000008820514,0.000003169482,0.00003152978,0.0001207456,0.00001983823,0.0004250623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01122741,"threshold_uncertainty_score":0.02232409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06294560519967733,"score_gpt":0.296177222198401,"score_spread":0.2332316169987236,"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."}}