We are not alone – other groups are producing great data on form and function of cities, which may suit your needs. Here is a selection.
Under construction.
Urban mask layers
| Dataset | Description/Properties | Reference | Data Access |
|---|---|---|---|
| Global Human Settlement Layer (GHSL) | Global multitemporal built-up layer from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014), spatial resolution of 30 m | Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M., Julea, A., Kemper, T., Soille, P., Syrris, V. (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. JRC Technical Reports | GHSL Download |
| Global Urban Footprint (GUF) | Global buildup layer from 180 000 TerraSAR-X and TanDEM-X scenes, spatial resolution of 0.4 arcsec (~12 m). | Esch, T., Heldens, W., Hirner, A., Keil, M., Marconcini, M., Roth, A., et al. (2017). Breaking new ground in mapping human settlements from space–The Global Urban Footprint. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 30–42. | GUF Download |
| World Settlement footprint | Global map of human settlement, derived from 700,000 Copernicus Sentinel-1 radar and Landsat-8 multispectral satellite images, spatial resolution of 10 m | ESA (2019). Applications - Mapping our global human footprint. https://bit.ly/35pqWzD | |
| ESA CCI Land Cover | Global land cover map, derived from the full Envisat MERIS archives (2003-2012) and SPOT Vegetation time series (1998-2012), spatial resolution 300 m | Bontemps, S., Boettcher, M., Brockmann, C., Kirches, G., Lamarche, D., et al. (2015). Multi-year Gloabl Land Cover mapping at 300 m and characterization for climate modelling: achievements of the land cover component of the ESA Climate Change Initiative. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 323-328. | CCI Land Cover Download |
| Global artificial impervious area (GAIA) | Annual global artificial impervious area maps derived from the full archive of Landsat images (1985-2018) at 30 m resolution | Gong P., et al., (2020). Annual maps of global artificial impervious area (GAIA) between 1985 and 2018, Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111510 | GAIA Download |
| Imperviousness Density (IMD) Europe | High resolution layer of imperviousness Density for Europe, derived automatically from calibrated NDVI (Sentinel-1 and 2 data) at 20 m and 100 m resolution for the years 2006, 2009, 2012 and 2015 | European Environment Agency (EEA) (2018). Imperviousness Density (IMD) 2015. | Imperviousness Density Europe Download |
| Urban imperviousness product U.S. | Urban percentage imperviousness product for the years 2001, 2006, 2011 and 2016 from the National Land Cover Database 2016, 30 m resolution, based on nighttime lights products, Landsat imagery and regression tree models | Yang, L. et al. (2018). A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS J. Photogramm. Remote. Sens. 146, 108–123, 10.1016/j.isprsjprs.2018.09.006 | Urban imperviousness U.S. Download |
| Global Urban Change Dataset | High spatiotemporal resolution mapping of global urban change for the years 1985 to 2015 at 30 m resolution, derived from surface reflectance data from Landsat Satellites | Liu, X., Huang, Y., Xu, X. et al. (2020): High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nat Sustain. https://doi.org/10.1038/s41893-020-0521-x | Global Urban Change Download |
| Facebook High Resolution Settlement Layer (HRSL) | Human population distribution estimates for 2015 derived from census data and high-resolution (0.5m) satellite imagery from DigitalGlobe, computer vision techniques were used to classify blocks of optical satellite data as settled (containing buildings) or not, resolution 30 m | Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University (2016). High Resolution Settlement Layer (HRSL). | Facebook HRSL Download |
Building data
| Dataset | Description/Properties | Reference | Data Access |
|---|---|---|---|
| Building Heigth 2012 Europe | 10 m high resolution layer with height information for 2012 based on IRS-P5 stereo images and derived datasets like the digital surface model, the digital terrain model and the normalized DSM | European Environment Agency (EEA) (2018). Urban Atlas—Building Height 2012. | Building Height Europe Download |
| Rasterized building footprints for the U.S. | National dataset of building footprints for 2018 based on vector building dataset by Bing Maps Team, transformed to raster dataset with a High Perfomance Computing developed algorithm, 30 m resolution Code to produce the data available here | Heris, M.P., Foks, N., Bagstad, K., and Troy, A., (2020). A national dataset of rasterized building footprints for the U.S.: U.S. Geological Survey data release Heris, M.P., Foks, N., Bagstad, K.,Troy, A., and Ancona, Z. (2020): A rasterized building footprint dataset for the United States. In: Scientific Data 7:207 | Building Footprints U.S. Download |
| U.S. national categorical mapping of building heights | Categorical mapping of building heights in block groups derived from the NASA Shuttle Radar Topography Mission in 2000, 30 m resolution, shapefile format | Falcone, J.A., (2016). U.S. national categorical mapping of building heights by block group from Shuttle Radar Topography Mission data: U.S. Geological Survey data release | Building Height U.S. Download |
| Austin Building Footprints 2013 | Building footprints shapefile for the city of Austin, Texas derived from 2012/2013 Orthoimagery and 212 Lidar, Public Domain License | Communication and Technology Management Department (2013). Building Footprints Year 2013 [Dataset]. City of Austin, Texas Open Data Portal | Building Footprints Austin Download |
| Des Moines Building Footprints | Dataset provided by the city of Des Moines, Iowa Data are in Iowa State Plane South, NAD83 | City of Des Moines (2019). City of Des Moines GIS Data. | Building Footprints Des Moines Download |
| L.A. County Building Outlines | Building outline dataset for the L.A. county including information on building height, building area and building footprint, generated from sterei imagery | County of Los Angeles (2016). Countywide Building Outlines - 2014 Update - Public Domain Release. | Building Outlines L.A. Download |
| NYC Building Footprints | Building footprint dataset for New York City including information on roof height above ground, derived from annually captured aerial imagery, data published in 2016, Public License | Department of Information Technology & Telecommunication (2016). Building Footprints. NYC OpenData | Building Footprints NYC Download |
| Historic settlement data HISDAC-US | Historic settlement data for the continental U.S. covering the time period 1810-2015, derived from Zillow’s ZTRAX database, temporal resolution of 5 years and spatial resolution of 250 m, Public Domain Dedication License | Leyk, S., Uhl, J. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Sci Data 5, 180175. https://doi.org/10.1038/sdata.2018.175 | Historical Built-up Intensity Download First Built-up Year Download |
| SEMCOG Building Footprints | Southeast Michigan Building footprint layer as of April 2015 with detailed information on buildings including building height, derived from 2010 and 2015 aerial images, download for each county available from ArcGIS Hub | SEMCOG (2016): SEMCOG Building Footprints. https://maps.semcog.org/BuildingFootprints/BuildingsDocumentation071916.pdf | SEMCOG Building Footprints Access |
| Chicago Building Footprints | Building Footprint dataset of Chicago as of August 2015, including detailed information about the buildings, ie. number of stories | City of Chicago (2015): Building Footprints (current) | Building Footprints Chicago Download |
| Boulder Building Footprints | Building footprint dataset of the city of Boulder with information on building height and ground elevation, creative commons license | City of Boulder Open Data (2020): Buildings in the City of Boulder | Building Footprints Boulder Download |
| Fort Collins Building Footprints | Building footprints dataset of the City of Fort Collins including information on building height, last updated 2017 | CFC Open Data (2017): Building Footprints for the City of Fort Collins | Building Footprints Fort Collins Download |
| Norman Building Footprints | Building Footprints dataset of the city of Norman including information on building height | City of Norman Open Data (2019): Building Footprints. | Building Footprints Norman |
| Abuquerque Building Footprints | Dataset with 3D Building structure outline in Bernalillo County and the City of Albuquerque, dataset derived from 2010 MRCOG LiDAR point cloud data and 2012 MRCOG Stereo Image Auto-Correlation data. | City of Albuquerque Open Data (2016): Building Footprints | Building Footprints Albuquerque Download |
| San Francisco Building Footprints | Building Footprints dataset for the city of San Francisco with detailed information on the buildings including building heights, derived from a 3D building model and building masses dataset | City and County of San Francisco, Department of Technology (2020): Building Footprints | Building Footprints San Francisco Download |
| Miami 3D Building Models | 3D Buildings models dataset for the Miami-Dade County, available as a Filegeodatabase | Miami-Dade County, Florida (2018): Miami-Dade County 3D buildings models data | Building Footprints Miami Download |
| Sarasota Building Footprints | Building footprint dataset for Sarasota County including information on base elevation and roof elevation | Sarasota County GIS Cadastral Layers Group (2020): The Building Footprint layer for Sarasota County. | Building Footprints Sarasota Download |
| Boston Building Footprints | Building footprints dataset for the city of Boston, derived from 2011 planimetric buildings, SAM (Street and Address Management) buildings, and BRA buildings datasets | City of Boston (2014): City of Boston building footprints | Building Footprints Boston Download |
| Roanoke Building Footprints | Building footprints dataset for Roanoke county including building height information, derived from aerial digital images | County of Roanoke (2020): Building Footprints | Building Footprints Roanoke Download |
| Santa Clara Building Structure | Dataset on building structure for the city of Santa Clara including building height, derived from 2015 Aerial Imagery | City of Santa Clara Open Data 2.0 Group (2015): City of Santa Clara Building Structure - 2015 | Building Structure Santa Clara Download |
| Sauk County Building Footprints | Dataset of building footprints for Sauk County including building height | Sauk County Open Data (2019): BuildingFootprints | Building Footprints Sauk Download |
| Henderson Building Footprints | Building footprints dataset for the city of Henderson, including information on building height | City of Henderson, Nevada GIS Data Portal (2020): Building Footprints | Building Footprints Henderson Download |
| Lincoln Building Footprints | Building footprint dataset for the city of Lincoln from 2016 with detailed information on the buildings including building height | Lincoln Open Data (2018): Building Footprints | Building Footprints Lincoln Download |
| Sioux Falls Building Footprints | Building footprints dataset for the city of Sioux Falls including ground elevation and building top elevation information | City of Sioux Falls Open Data - Property (2020): Building Footprints | Building Footprints Sioux Falls Download |
| Arlington Building Structure | Builing Structure Dataset for the city of Arlington inluding information on building height and building area | Arlington Open Data (2018): ArlingtonMA Structure | Building Structure Arlington Download |
| Washington DC 3D Buildings | 3D Buildings dataset fot the city of Washington, DC, updated 2005 3D dataset with 2010 aerial images | DCGISopendata (2010): Buildings in 3D | 3D Buildings Washington DC Download |
| Cambridge Building Footprints | Dataset of building footprints for the city of Cambridge, including information on elevation of roof and base, map developed using digital photogrammetric techniques | City of Cambridge GIS (2018): Buildings | Building Footprints Cambridge Download |
| European Settlement Map 2015 | spatial raster dataset of human settlements in Europe based on Copernicus Very High Resolution optical coverage for reference year 2015, available at 2.5 m and 10 m resolution | Sabo, F., Corbane, C., Politis, P., Kemper, T. (2019): The European Settlement Map 2019 release, EUR 29886 EN, Publications Office of the European Union, Luxembourg, doi:10.2760/25824 | European Settlement Map Download |
| Microsoft Building Footprints | High-quality building footprint data derived from Bing imagery through Deep Neural Networks, ResNet34 with RefineNet and a polygonization algorithm, resolution for Canada and US is 1ft/pixel at a 256x256 pixel size, resolution for Tanzania and Uganda is 30cm/pixel, Open Data Commons Open Database License (ODbL) | Microsoft Bing Maps (2019): Microsoft Building Footprints. https://www.microsoft.com/en-us/maps/building-footprints | MS US Building Footprints Download MS Canada Building Footprints Download MS Uganda Tanzania Building Footprints Download |
Tree datasets
| Dataset | Description/Properties | Reference | Data Access |
|---|---|---|---|
| Tree dataset OpenTrees | Dataset of municipal street and park trees derived from city's open tree databases | Bennett, S. (2020): OpenTrees.org | OpenTrees Access |
| Street Tree Layer (STL) Europe | Rows of trees or patches of trees covering 500 m² over artifical surfaces. It is included in the Urban Atlas for Functional Urban Areas (FUA) | European Environmental Agency (EEA) (2020): Urban Atlas: Street Tree Layer (STL) 2018 | Street Tree Layer Download |
Urban Population Data
| Dataset | Description/Properties | Reference | Data Access |
|---|---|---|---|
| Human Planet Urban Centres Database | 4D global database of cities from the threshhold of 50000 inhabitants, derived from the Global Human Settlement data; database offers information about location, extent, several attributes like population and greenness and information over time for each city | European Commission, Joint Research Centre, Atlas of the Human Planet 2018 –A World of Cities, EUR 29497 EN, European Commission, Luxembourg, 2018, doi:10.2760/124503, JRC114316. | Urban Centres Database Download |