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We introduce a novel inverse procedural modeling algorithm for urban areas that addresses the problem of spatial data quality and uncertainty. Our method is fully automatic and produces a 3D approximation of an urban area given satellite imagery and global-scale data, including road network, population, and elevation data. By analyzing the values and the distribution of urban data, e.g., parcels, buildings, population, and elevation, we construct a procedural approximation of a city at a large-scale. Our approach has three main components: (1) procedural model generation to create parcel and building geometries, (2) parcel area estimation that trains neural networks to provide initial parcel sizes for a segmented satellite image of a city block, and (3) an optional optimization that can use partial knowledge of overall average building footprint area and building counts to improve results.<\/jats:p>\n          <jats:p>\n            We demonstrate and evaluate our approach on cities around the globe with widely different structures and automatically yield procedural models with up to 91,000 buildings, and spanning up to 150 km\n            <jats:sup>2<\/jats:sup>\n            . We obtain both a spatial arrangement of parcels and buildings similar to ground truth and a distribution of building sizes similar to ground truth, hence yielding a statistically similar synthetic urban space. We produce procedural models at multiple scales, and with less than 1% error in parcel and building areas in the best case as compared to ground truth and 5.8% error on average for tested cities.\n          <\/jats:p>","DOI":"10.1145\/3423422","type":"journal-article","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T01:17:32Z","timestamp":1603847852000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data"],"prefix":"10.1145","volume":"7","author":[{"given":"Xiaowei","family":"Zhang","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"given":"Aly","family":"Shehata","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5293-2112","authenticated-orcid":false,"given":"Bedrich","family":"Benes","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"given":"Daniel","family":"Aliaga","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"key":"e_1_2_1_1_1","article-title":"TerraNNI: Natural neighbor interpolation on 2D and 3D grids using a GPU","volume":"2","author":"Agarwal Pankaj K.","year":"2016","unstructured":"Pankaj K. 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