{"id":"W4248876383","doi":"10.5194/isprsarchives-xli-b2-349-2016","title":"GEOSPATIAL MODELLING APPROACH FOR 3D URBAN DENSIFICATION DEVELOPMENTS","year":2016,"lang":"en","type":"article","venue":"˜The œinternational 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":6,"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; Raster data; Dimension (graph theory); Geographic information system; Geomatics; Architecture; Spatial analysis; Data science; Geography; Civil engineering; 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":[0.0003819291,0.0006359063,0.0005064302,0.001421857,0.0006391369,0.002078003,0.001301598,0.001118168,0.003763282],"category_scores_gemma":[0.0007969421,0.0004880067,0.001464503,0.001515292,0.0006832764,0.00103306,0.001373358,0.000611999,0.0005977144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001294887,"about_ca_system_score_gemma":0.001121366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03154472,"about_ca_topic_score_gemma":0.0326286,"domain_scores_codex":[0.9997446,0.00007346983,0.00002140628,0.00004324563,0.00009486337,0.00002233937],"domain_scores_gemma":[0.9998001,0.00007706576,0.0000274229,0.00002295594,0.00005674885,0.00001575055],"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":[0.000008671692,0.00001246432,0.0005689356,0.00003364808,0.0000177992,0.00009909245,0.0001384128,0.9623075,0.0008396636,0.02834108,0.0004363737,0.007196298],"study_design_scores_gemma":[0.000002380875,0.000004383836,0.0001491824,0.000009264073,0.000006012757,0.00002963375,0.00005141343,0.990302,0.0001989697,0.005567612,0.003671142,0.000008022735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01918263,0.0002169229,0.9670219,0.000231739,0.00004010053,0.00009021116,0.0006510819,0.0005680046,0.0119973],"genre_scores_gemma":[0.5799153,0.0008756921,0.4087107,0.00008790594,0.00003070644,0.0004470421,0.001179638,0.0002483442,0.008504706],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03154472,"threshold_uncertainty_score":0.06272221,"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."}}