Projected Urban Morphology of the Los Angeles Area by the Year 2100

This data set was generated from dual generative adversarial networks that generates building footprints fraction and average building heights using land cover data as the primary conditional input. Model training used c2015 land cover data and building footprints and heights data. Building footprints and heights were sourced from the Model America data set (version 1) accessible via DOI: 10.15485/2283980. Annual 2015 land cover data was based on the National Land Cover Database (NLCD) Collection 1, accessible via DOI: 10.5066/P94UXNTS. The dataset contains 192 files in the GeoTiff format at 30 m spatial resolution containing urban morphology projections for the year 2100 for: two building attributes, i.e., average building heights (BH) and building footprint fractions (BF) (2), two SSP scenarios (SSP3 and SSP5) (2), two population (original and updated) scenarios (2), two developed land intensification scenarios (intensification and weighted average) (2), four intensification levels (low, med, high, and very high) (4), and for each of these scenarios three generative adversarial network outputs (3). File naming convention encodes this metadata directly within the filename to facilitate easy identification, sorting, and retrieval. Each filename follows the following structure: [Building Attribute]-[SSP Scenario]-[Population Scenario]-[Intensification Scenario]-[Intensification Levels]-[Year]-[GANOutputID].tif. Each GeoTiff file has a size of 4280, 3632 [width, height], and has a projected Albers Equal Area Coordinate Reference System (CRS) with values for BF ranging from 0 to 1 and for BH from 0 to 75 meters. Pixels values are stored with a thirty two bit floating point data type. The dual generative adversarial network architecture used to generate this dataset is based on the original methodology proposed in Goodfellow et al. (2020). Each of the GeoTiff file was read back in python and R programming languages, using rasterio and raster packages, respectively, and . Authors: Pandey, Bhartendu [ORNL] (ORCID:0000000237125961); Dumas, Melissa [ORNL] (ORCID:0000000233190846). DOE Contract: AC05-00OR22725