{"id":"W2058840065","doi":"10.1109/jstars.2014.2336236","title":"Fast and Efficient Evaluation of Building Damage From Very High Resolution Optical Satellite Images","year":2014,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Satellite; Satellite image; Image resolution; Classifier (UML); Scale (ratio); Remote sensing; Pattern recognition (psychology); Geology; Cartography; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005774893,0.000614073,0.0005902366,0.002672376,0.0002170153,0.0007238108,0.0005130201,0.0006027521,0.001342596],"category_scores_gemma":[0.001507306,0.0002018769,0.0003390649,0.0009428867,0.0002237861,0.0009394935,0.0005143539,0.000345744,0.001187786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002701331,"about_ca_system_score_gemma":0.0003033445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520302,"about_ca_topic_score_gemma":0.003664065,"domain_scores_codex":[0.9994172,0.00005979002,0.00002612526,0.00008294106,0.0003503905,0.00006362855],"domain_scores_gemma":[0.99903,0.0002234354,0.0001842572,0.0001408773,0.0003586644,0.00006274454],"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":[0.0005674491,0.000395062,0.03771438,0.0002990109,0.0001524879,0.0002013886,0.00009906814,0.03356851,0.1976565,0.000436364,0.002774385,0.7261354],"study_design_scores_gemma":[0.00002838211,0.0004352031,0.1568819,0.00003268232,0.00007941118,0.0005879191,0.0002104784,0.7307864,0.1077581,0.0007156664,0.002434056,0.00004983283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5573034,0.0005812885,0.433402,0.0001044392,0.00005959232,0.0002453976,0.001149449,0.004007278,0.003147044],"genre_scores_gemma":[0.807328,0.0003384732,0.1877968,0.00003880309,0.00004785121,0.0001052584,0.002270041,0.0001466746,0.001928066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002672376,"threshold_uncertainty_score":0.004491448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02236946444668536,"score_gpt":0.2331079750206208,"score_spread":0.2107385105739354,"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."}}