{"id":"W2214979521","doi":"10.5623/cig2015-206","title":"Semi-Automated Building Footprint Extraction From Orthophotos","year":2015,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Esri (Canada); University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Orthophoto; Footprint; Lidar; Computer science; Segmentation; Artificial intelligence; Computer vision; Remote sensing; Aerial image; Image segmentation; Software; Land cover; Ranging; Geography; Image (mathematics); Land use; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002399734,0.001197642,0.0009777113,0.002724595,0.0003106534,0.0009035144,0.0007833369,0.000605335,0.00393107],"category_scores_gemma":[0.0007488525,0.0007096762,0.000791375,0.001859108,0.0002447531,0.0007139678,0.000906846,0.0006077526,0.00417609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001512302,"about_ca_system_score_gemma":0.0008817209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005207567,"about_ca_topic_score_gemma":0.01236776,"domain_scores_codex":[0.9996424,0.0000280686,0.00001997466,0.00008798252,0.0001537989,0.00006773774],"domain_scores_gemma":[0.9995617,0.00008965345,0.00004559724,0.000092838,0.0001818365,0.00002829718],"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":[0.0002401433,0.000195606,0.005312588,0.000785729,0.0001501419,0.0003257767,0.0002424791,0.02103837,0.1850799,0.001048439,0.01099645,0.7745843],"study_design_scores_gemma":[0.00009104809,0.0002110724,0.07675978,0.0002482265,0.0002270616,0.0015487,0.0007778259,0.74786,0.1293634,0.005427167,0.0373264,0.0001592745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0882137,0.0007512461,0.883804,0.0001006222,0.0001072647,0.0003701413,0.006618104,0.01559305,0.004441772],"genre_scores_gemma":[0.3158635,0.0008151795,0.6617035,0.00008765091,0.00005344484,0.000292922,0.01607171,0.0008104356,0.004301737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005207567,"threshold_uncertainty_score":0.01315075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01812635264324132,"score_gpt":0.2684757896981714,"score_spread":0.2503494370549301,"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."}}