{"id":"W6922074832","doi":"10.1139/geomat-2021-0006","title":"Waterloo Building Dataset: A city-scale vector building dataset for mapping building footprints using aerial orthoimagery","year":2021,"lang":"","type":"other","venue":"TSpace","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; University of Waterloo","keywords":"Footprint; Orthophoto; Key (lock); Aerial survey; Deep learning; Baseline (sea); Aerial imagery; Image resolution","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003658014,0.002953422,0.001217533,0.002834702,0.001182713,0.001388741,0.003340434,0.001374202,0.01155708],"category_scores_gemma":[0.001611367,0.0005466971,0.001323002,0.00549354,0.000837224,0.0009790615,0.001634597,0.001692252,0.01290855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003167376,"about_ca_system_score_gemma":0.00379989,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4786805,"about_ca_topic_score_gemma":0.7834807,"domain_scores_codex":[0.9990434,0.00006192768,0.00004896635,0.0002376447,0.0004350652,0.0001729789],"domain_scores_gemma":[0.9992587,0.00007529245,0.00004968451,0.0001884431,0.0003366524,0.00009126805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001429417,0.0002090547,0.007512833,0.0007618167,0.0001718218,0.0003358327,0.0001739158,0.004500024,0.002506632,0.001390241,0.9358803,0.04641456],"study_design_scores_gemma":[0.0002851296,0.00008421723,0.04162629,0.0003923117,0.0001084344,0.0005502573,0.00082684,0.03976143,0.007485287,0.003369441,0.9053227,0.0001877263],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01510932,0.0008425954,0.006327782,0.0003659721,0.000134688,0.0002852423,0.9591451,0.008372814,0.009416493],"genre_scores_gemma":[0.01049292,0.0002098763,0.006932067,0.00006156221,0.00001292613,0.0001248695,0.9799263,0.0002354371,0.002004015],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5213195,"threshold_uncertainty_score":0.9517886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05237260171071949,"score_gpt":0.3413082966330374,"score_spread":0.2889356949223179,"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."}}