{"id":"W2411632075","doi":"10.5194/isprs-archives-xli-b2-349-2016","title":"GEOSPATIAL MODELLING APPROACH FOR 3D URBAN DENSIFICATION DEVELOPMENTS","year":2016,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"3D Modeling in Geospatial Applications","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geospatial analysis; Raster graphics; Computer science; Key (lock); Cellular automaton; Dimension (graph theory); Raster data; Architecture; Geomatics; Geographic information system; Spatial analysis; Data science; Civil engineering; Architectural engineering; Geography; Cartography; Computer graphics (images); Artificial intelligence; Remote sensing; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004368353,0.0006042312,0.0004970704,0.001444087,0.0006326534,0.002021302,0.001058887,0.001121683,0.003924435],"category_scores_gemma":[0.0009013995,0.0004640885,0.001297467,0.001299609,0.0006952732,0.0008654127,0.001183105,0.0005758152,0.0004777331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440915,"about_ca_system_score_gemma":0.001058234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0338951,"about_ca_topic_score_gemma":0.03285629,"domain_scores_codex":[0.9997527,0.00007990243,0.0000201746,0.00004117016,0.00008258477,0.00002351266],"domain_scores_gemma":[0.9997354,0.000110808,0.00003387785,0.00002622731,0.00007500206,0.00001871573],"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":[0.000008291293,0.00001101519,0.0005520333,0.0000229273,0.00001265015,0.00007532167,0.00008368304,0.9792447,0.0005711057,0.0146749,0.0002375153,0.004505861],"study_design_scores_gemma":[0.000001741475,0.000003255241,0.0001203323,0.000006188412,0.000003492554,0.00001631766,0.00002960037,0.9957825,0.0001327914,0.002426579,0.00147258,0.000004657243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04501732,0.0002589952,0.9378372,0.0002831852,0.0000491454,0.0001172121,0.0007888189,0.0006938149,0.01495425],"genre_scores_gemma":[0.7528,0.0004887133,0.2391452,0.00006420605,0.00001960271,0.0003247758,0.0008163126,0.0001591815,0.006182052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0338951,"threshold_uncertainty_score":0.06739563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02255694903582453,"score_gpt":0.2391277533851882,"score_spread":0.2165708043493637,"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."}}