Refer to the table below for a detailed list of all the data layers available in Google Earth. Note that data layers may change periodically throughout the year. Check this page regularly for updates.
| Data layer | Description | Source(s) | Plan | Coverage |
|---|---|---|---|---|
| Administrative area level 1 | First-order civil entities below the country level, like US states, provinces, and other areas of similar size. Not all nations use these administrative levels. | Google Maps | Standard | United States |
| Administrative area level 2 | Second-order civil entities below the country level, like US counties and other areas of similar size. Not all nations use these administrative levels. | Google Maps | Standard | United States |
| Administrative area level 3 | Third-order civil entities below the country level, like cities and other areas of similar size. Not all nations use these administrative levels. | Google Maps | Standard | United States |
| Agricultural landscape understanding | Leverages satellite imagery and machine learning to draw agricultural boundaries of fields, the basic unit of agriculture and essential in creating meaningful insights. With field segments established, the model can determine the acreage of farm fields. Similarly, other landscape elements like water bodies and vegetation can be identified, which can help with drought contingency planning. | Standard | Portions of APAC | |
| Brazil Forest Imagery Dataset 2008 | The Brazil Forest Imagery Dataset 2008 is a 5-meter resolution (where available) visual imagery basemap of Brazil, created using SPOT 2, 4, and 5 satellite data from the 2008 era. Produced by Google, this dataset is designed to support implementation of the Brazil Forest Code by providing historical imagery to understand the extent of deforestation and native vegetation on private rural properties as of July 22, 2008. This first version of the basemap prioritizes coverage for private rural properties for regions of Brazil that have historically been at the frontier of deforestation, such as the Amazon rainforest. The basemap predominantly features 2008 imagery, utilizing 2007 or 2009 data only where 2008 coverage was unavailable. |
Google Earth Engine | Standard | Brazil |
| Cycling trip percentage | Shows the monthly percentage of trip segments that are completed by bike, based on anonymized Google location data. Values are measured by S2 Cell Level 11 boundaries: generally about 20 square kilometers, or roughly equivalent to the size of a small suburban area or a large neighborhood. Only trips that begin and end within the boundary are included in the calculation. The latest data is from December 2025. |
Google Maps | Professional Advanced | Global |
| Digital elevation model (Copernicus GLO-30) |
A digital elevation model (DEM) that represents the top-reflective surface of the Earth including buildings, infrastructure and vegetation at 30 meter resolution. Data were acquired through the TanDEM-X mission between 2011 and 2015. |
Google Earth Engine | Standard | Global |
| Driveway counts (by postal code) | The number of driveways within each postal code (not per mile of roadway), estimated by satellite imagery. Includes both private driveways (for example, next to a single family house) and public or commercial driveways (for example, leading to a park or mall plaza). | Professional Advanced |
Metro areas in: Australia Belgium Canada Denmark France Germany Hungary Italy Japan Mexico Netherlands New Zealand Norway Spain Sweden Switzerland United Kingdom United States |
|
| Driveway counts (by US census tract) | The number of driveways within each US census tract (not per mile of roadway), estimated by satellite imagery. Includes both private driveways (for example, next to a single family house) and public or commercial driveways (for example, leading to a park or mall plaza). | Professional Advanced | United States | |
| Elevation contours (20m intervals) | Contour lines representing the topography of Earth at 20-meter intervals above sea level. Zoom in to visualize and interact with elevation contours on the map. Contour lines are based on inferred data, which may contain errors, and don't equal onsite or survey-grade information. | Google Earth Engine | Professional | Global |
| Elevation contours (40m intervals) |
Contour lines representing the topography of Earth at 40-meter intervals above sea level. Zoom in to visualize and interact with elevation contours on the map. Contour lines are based on inferred data, which may contain errors, and don't equal onsite or survey-grade information. |
Google Earth Engine | Standard | Global |
| EV charging station counts (by postal code) | The number of EV charging stations aggregated within each postal code boundary. | Google Maps | Professional Advanced |
Åland Islands Andorra Australia Belgium Bulgaria Canada Colombia Croatia Czechia Denmark Egypt Estonia Faroe Islands Finland France Germany Gibraltar Guadeloupe Hungary Iceland India Italy Japan Latvia Liechtenstein Lithuania Martinique México Monaco Netherlands New Zealand Norway Paraguay Réunion Russia Saint Helena Saint Pierre and Miquelon Slovakia Slovenia Spain Svalbard and Jan Mayen Sweden Switzerland Thailand Turks and Caicos Islands United Kingdom United States Uruguay Venezuela |
| EV charging station counts (by US census tract) | The number of EV charging stations aggregated within each census tract boundary. | Google Maps | Professional Advanced | United States |
| EV charging station locations | Includes the address, operating company, and available charging ports at each location. Each point in this layer represents a single station. | Google Maps | Professional Advanced | Global |
| EV charging station search interest | Represents monthly searches on Google Maps mobile within each census tract boundary. Numbers are aggregated based on coarsened user locations and have been anonymized. The anonymized aggregates are then converted to an index from 0 to 100, where 100 signifies the highest search interest and 0 the lowest. |
Google Maps | Professional Advanced | United States |
| Forest cover | Represents where forests are present (or absent) for the year 2020 at 10-meter resolution. Forest is defined as land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach those thresholds in situ, excluding land predominantly under agricultural or urban land use. This map of forest cover was created by combining existing global spatial layers. |
Google Earth Engine European Commission, Joint Research Centre (JRC) |
Standard | Global |
| Forest cover subtypes | Represents where three types of forest are present – primary forest, naturally regenerating forest, and planted forest (including plantation forest) – for the year 2020 at 10-meter resolution. Forest is defined as land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach those thresholds in situ, excluding land predominantly under agricultural or urban land use. This map of forest cover was created by combining existing global spatial layers. |
Google Earth Engine European Commission, Joint Research Centre (JRC) |
Standard | Global |
| Household income | The median household income within each census tract boundary. | United States Census (2020) | Standard | United States |
| Land surface temperature (by US census tracts) | The average of surface temperature values (celsius) within each census tract, from the United States Geological Survey (USGS) Landsat 7,8,9 during the three warmest months of the year (for example, June, July, August in the northern hemisphere) for six years (2018-2023). |
USGS Landsat 7, 8, 9 | Standard | United States |
| Localities |
Civil entities like cities, towns, and municipalities, as defined by local city governments. Not seeing full coverage? Try adding the Administrative area level 3 data layer to see more geographic divisions of similar size. |
Google Maps | Standard | United States |
| Inbound monthly vehicle trips |
The estimated total monthly vehicle trips inbound to each census tract, limited to trips within each greater metropolitan area. These metrics have been anonymized with differential privacy techniques, aggregated at the US Census Tract level, then scaled up to represent the larger traveling population. |
Google Maps | Professional Advanced |
Atlanta Boston Chicago Dallas Denver Houston Las Vegas Los Angeles Miami New York Orlando Philadelphia San Diego San Francisco Seattle Washington, DC |
| Intrabound monthly vehicle trips | The estimated total monthly vehicle trips intrabound within each census tract. These metrics have been anonymized with differential privacy techniques, aggregated at the US Census Tract level, then scaled up to represent the larger traveling population. |
Google Maps | Professional Advanced |
Atlanta Boston Chicago Dallas Denver Houston Las Vegas Los Angeles Miami New York Orlando Philadelphia San Diego San Francisco Seattle Washington, DC |
| Inundation (flooding) history | Represents how often places around the world have been wet from 1999 to 2020 at 128-meter resolution. Places are measured as wet 0.5%, 1%, or 5% of the time. This layer was created by Google based on the GLAD dataset and using satellite imagery. | Professional | Global | |
| Land cover (WorldCover v100) | A global land cover map for 2020, measured at 10-meter resolution based on Sentinel-1 and Sentinel-2 data from the European Space Agency (ESA). It comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5). | Google Earth Engine | Standard | Global |
| Land cover (WorldCover v200) | A global land cover map for 2021, measured at 10-meter resolution based on Sentinel-1 and Sentinel-2 data from the European Space Agency (ESA). It comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5). | Google Earth Engine | Standard | Global |
| Land use zones (Australia) | Zoning information | Zoneomics | Professional Advanced | Australia |
| Land use zones (Canada) | Zoning information | Zoneomics | Professional Advanced | Canada |
| Land use zones (United States) | Zoning information | Zoneomics | Professional Advanced | United States |
| Multi-dwelling unit counts (by US census tract) | A multi-dwelling unit is defined as a building or group of buildings that contain multiple separate housing units. | Google Maps, American Community Survey |
Professional Advanced | United States |
| Outbound monthly vehicle trips | The estimated total monthly vehicle trips outbound from each census tract, limited to trips within each greater metropolitan area. These metrics have been anonymized with differential privacy techniques, aggregated at the US Census Tract level, then scaled up to represent the larger traveling population. |
Google Maps | Professional Advanced |
Atlanta, GA Boston, MA Chicago, IL Dallas, TX Denver, CO Houston, TX Las Vegas, NV Los Angeles, CA Miami, FL New York, NY Orlando, FL Philadelphia, PA San Diego, CA San Francisco, CA Seattle, WA Washington, DC |
| Population (Global Human Settlement Layer by US census tract) | Projected population counts and density (per square meter), as aggregated within each census tract boundary. | European Commission, Joint Research Centre (JRC) | Standard | United States |
| Population (Global Human Settlement Layer by postal code) | Projected population counts and density (per square meter), as aggregated within each census tract boundary. | European Commission, Joint Research Centre (JRC) | Standard |
Åland Islands Andorra Australia Belgium British Indian Ocean Territory Bulgaria Canada Christmas Island Cocos (Keeling) Islands Colombia Croatia Czechia Denmark Egypt Estonia Falkland Islands Faroe Islands Finland France Germany Gibraltar Guadeloupe Hungary Iceland India Italy Japan Kosovo Latvia Liechtenstein Lithuania Martinique Mexico Monaco Netherlands New Zealand Northern Mariana Islands Norway Paraguay Pitcairn Islands Réunion Russia Saint Barthélemy Saint Helena Saint Martin Saint Pierre and Miquelon Slovakia Slovenia South Georgia & South Sandwich Islands Spain Svalbard and Jan Mayen Sweden Switzerland Thailand Turks and Caicos Islands United Kingdom United States Uruguay Vatican City Venezuela |
| Population (WorldPop, by postal code) | Projected population counts and density (per square meter), as aggregated within each postal code boundary. | WorldPop | Standard |
Åland Islands Andorra Australia Belgium British Indian Ocean Territory Bulgaria Canada Christmas Island Cocos (Keeling) Islands Colombia Croatia Czechia Denmark Egypt Estonia Falkland Islands Faroe Islands Finland France Germany Gibraltar Guadeloupe Hungary Iceland India Italy Japan Kosovo Latvia Liechtenstein Lithuania Martinique Mexico Monaco Netherlands New Zealand Northern Mariana Islands Norway Paraguay Pitcairn Islands Réunion Russia Saint Barthélemy Saint Helena Saint Martin Saint Pierre and Miquelon Slovakia Slovenia South Georgia & South Sandwich Islands Spain Svalbard and Jan Mayen Sweden Switzerland Thailand Turks and Caicos Islands United Kingdom United States Uruguay Vatican City Venezuela |
| Population (WorldPop, by US census tract) | Projected population counts and density (per square meter), as aggregated within each census tract boundary. | WorldPop | Standard | United States |
| Postal codes | Geographic boundaries as defined by the postal service. | United States Postal Service | Standard | United States |
| Rooftop reflectivity / albedo (by US census tract) | Average solar reflectivity – also known as albedo – of rooftops in a census tract. This is defined as the fraction of sunlight that is reflected from a surface. It is measured on a scale of 0 (absorbs all incident radiation) to 1 (reflects all incident radiation). Rooftop reflectivity on low-slope roofs is the average across low-slope roof segments. Low-slope roofs have pitches (measured as a ratio of the number of inches that the roof rises for every 12 horizontal inches) that are ≤ 2:12. Rooftop reflectivity on steep-slope roofs is the average across steep-slope roof segments. Steep-slope roofs have pitches that are > 2:12. |
Standard | Austin, TX Baltimore, MD Boston, MA Boulder, CO Colorado Springs, CO Los Angeles, CA Miami-Dade, FL Nashville, TN New York, NY Phoenix, AZ San Antonio, TX Stockton, CA Tempe, AZ Washington, DC | |
| Traffic signal level of service | Grades the level of service for intersections with traffic signals, based on vehicle delay times. These are calculated using anonymized and aggregated Google data. Grades span A-F and are available for each hour between 06:00–22:00. | Professional Advanced |
Atlanta, GA Boston, MA Charlotte, NC Chicago, IL Dallas, TX Denver, CO Detroit, MI Eustis, FL Houston, TX Los Angeles, CA Miami-Dade, FL Minneapolis, MS Nashville, TN New York, NY Ocala, FL Orlando, FL Philadelphia, PA Phoenix, AZ San Diego, CA San Francisco, CA Seattle, WA Tampa, FL Washington, DC Bologna, Italy |
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| Tree canopy percentage (by US census tract) | Tree canopy percentage is estimated as the percentage of pixels in a census tract that are categorized as "tree", based on an AI model that was trained to categorize pixels in high-resolution overhead imagery into a number of terrain types, such as "tree" or "road". | Standard | United States | |
| Tree canopy percentage (by postal code) | Tree canopy percentage is estimated as the percentage of pixels in a postal code that are categorized as "tree", based on an AI model that was trained to categorize pixels in high-resolution overhead imagery into a number of terrain types, such as "tree" or "road". | Standard |
Andorra Australia Belgium Bulgaria Canada Croatia Czechia Denmark Estonia Finland France Germany Hungary Iceland Italy Japan Latvia Liechtenstein Lithuania Mexico Monaco Netherlands New Zealand Norway Slovakia Slovenia Spain Sweden Switzerland United Kingdom United States |
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| US census tracts | Subdivisions, typically of a county or statistically equivalent entity, delineated by local participants as part of the US Census Bureau's Participant Statistical Areas Program. Boundaries normally follow physical features but can also follow administrative or other non-physical features. | United States Census | Standard | United States |
| US land parcels with zoning | Land parcels across the United States for which zoning information is available. | Zoneomics, Google Maps |
Professional Advanced | United States |
| US future land use | This color-coded data layer provides guidance from local municipalities on future land use, with detailed information embedded within each zone, for available towns only. | Zoneomics | Professional Advanced | United States |