_VERIFIED_ Download Building Footprints

Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses. The project is based in Ghana, with an initial focus on the continent of Africa and new updates on South Asia, South-East Asia, Latin America and the Caribbean.

For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the centre of the building. There is no information about the type of building, its street address, or any details other than its geometry.

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Building footprints are a key ingredient for estimating population density. In areas of rapid change, or where census information is out of date, population estimates are vital for many kinds of planning and statistics.

To plan the response to a flood, drought, or other natural disaster, it is useful to be able to assess the number of buildings or households affected. This is also useful for disaster risk reduction, e.g. to estimate the number of buildings in a particular hazard area.

In many areas buildings do not have formal addresses, which can make it difficult for people to access social benefits and economic opportunities. Building footprint data can help with the rollout of digital addressing systems such as Plus Codes.

Curious about why some buildings appear offset from the satellite imagery? Or why detection doesn't work as well in areas with dense or complex buildings? See details about data limitations and quality in the FAQs and technical report.

A deep learning model was trained to determine the footprints of buildings from high resolution satellite imagery. Our accompanying technical report describes the methodology used to generate the first version of the dataset. We however made further improvements for the subsequent versions, v2 and v3.

As the imagery in Google Maps is updated over time, the specific images used to identify these buildings are not necessarily the same images that are currently published in Google Maps. If there is a misalignment between these two sets of imagery, buildings displayed in the data explorer map may appear to be offset from the underlying imagery.

Despite having a diverse set of training data, some scenarios are challenging for the building detection pipeline, including: 1) geological or vegetation features which can be confused with built structures; 2) settlements with many contiguous buildings not having clear delineations; 3) areas characterised by small buildings, which can appear only a few pixels wide at the given image resolution; 4) rural or desert areas, where buildings constructed with natural materials tend to visually blend into the surrounding area; 5) areas with high-rise buildings: our model is trained to detect building rooftop rather than base and as a result, depending on satellite image's viewing angle, roofs of high rise buildings jump around which makes it challenging to track buildings across a stack of imagery in a given location. See the technical report for more details.

Imagery completeness errors: for some areas, up-to-date satellite imagery may not have been available, or there were buildings on the ground that were not visible from the satellite image, or there was cloud cover.

Detection errors: estimated precision and recall curves for our detection model, based on a held-out test set, are as shown below. For more details, including confidence score thresholds, click on the plot. A further breakdown based on density (fraction of image occupied by buildings) is also shown.

By choosing the confidence score threshold at which buildings are filtered out, the tradeoff between precision and recall can be controlled. We provide suggested thresholds with each download tile to obtain estimated 80% and 90% precision levels.

The dataset freshness is determined by the availability of the high-resolution source imagery which we use to detect buildings. While we have tried to include the most recent images possible, particularly in populated areas, in some cases, the most recent image for some location was several years old or not available to us at all. To look at freshness for a particular area, the Historical Imagery function in Google Earth Pro shows the specific dates and imagery (check for imagery before the inference date given in the version history below). Furthermore, we have not processed imagery for the entire continent: to check whether a particular region has been included, the dataset explorer map above visualises all buildings in the dataset.

The current version, v3, adds new regions in Latin America and the Caribbean (in addition to Africa, South Asia and South East Asia in the previous versions), has improved accuracy, and is based on more up-to-date satellite imagery. Click here to view differences in precision-recall curves. We further provide breakdown of precision-recall curves based on building density.

W. Sirko, S. Kashubin, M. Ritter, A. Annkah, Y.S.E. Bouchareb, Y. Dauphin, D. Keysers, M. Neumann, M. Cisse, J.A. Quinn. Continental-scale building detection from high resolution satellite imagery. arXiv:2107.12283, 2021.

Shapefile of footprint outlines of buildings in New York City. Please see the following link for additional documentation- -geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdPreviously posted versions of the data are retained to comply with Local Law 106 of 2015 and can be provided upon request made to Open Data.

Building footprints are useful because they provide detailed delineations of structures or parts of properties. This offers more insight than simple point of interest (POI) data. For example, when comparing building footprint vs. building area, the former lets you visualize what a building is shaped like and how much space it takes up relative to its surroundings. With the right metadata, it can also tell you whether a building is its own entity or a unit inside another building (i.e. spatial hierarchy). The latter can tell you how large a building is, but not how that area is distributed.

Another consideration is spatial hierarchy. Some basic address data, combined with basic POI or property data, can help trace a route to a particular building or point of interest. But it may not be much help when dealing with a building that has multiple smaller units inside it, such as a mall, apartment complex, or office tower. It can also not be precise enough information for a place such as a college or university campus, which is made up of multiple related buildings but can sometimes be classified as a singular point of interest.

Insurance companies require accurate and precise polygon data to evaluate the risk factors of buildings and other properties. As a start, they need to be able to model a building or area to identify potential hazard spots where an accident is more likely to occur. But there is much more they can do with building footprints.

Of course, understanding building footprint examples can also be helpful for government agencies trying to parcel out the use of city land. Knowing the dimensions and area a building takes up on a plot of land can help civil engineers plan infrastructure around it. That includes plumbing, electricity, sidewalks (and other transportation infrastructure), and constructs to aid with accessibility (like wheelchair ramps).

Studying building footprint GIS data also gives context to other geospatial data that urban planners might use. Chief among those is mobility data. Planners can look at the flow of population movements within a city on a daily or weekly basis, and assess how the layout of buildings may be affecting that. They can also identify which buildings people are congregating at, and where people are coming from to reach those buildings. All of this may suggest the need for improved transportation networks to make important or popular destinations more accessible. Or it may mean a city should build critical facilities closer to places where people are already active.

So the issue with building extraction was a combination of the needing to extract the RGB (1 2 3) bands AND needing to downsample the imagery from 2cm to 30cm, which is what the model was trained on. Once I did that, the output was much better (and I didn't need to save a new file). Extracting the bands also helped improve the model output for pavement crack detection as well, though I didn't need to downsample for that model.

Building footprints in Chicago. Metadata may be viewed and downloaded at The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

Everything is length by width in tiles. Cost to relocate a building is 50,000 bells. Your home is only 30,000 bells. You can only relocate one thing a day. You can cancel a relocation before you place the marker kit and get your bells back by speaking with Tom Nook. And then try and relocate the same/different building.

I couldn't find this info anywhere online, so I did testing by making a square pattern and then using the custom path tool to easily make a grid. Then I compared each building size on the grid, by asking Nook to move every building. You can copy this method to double check my math at home!

You can place a building over paths. So you can also easily use a grid pattern as a building placeholder. However be careful about it deleting nearby trees/fences/objects/flowers you wish to keep by using 'Let me imagine it' option when relocating.

Also, I just saw u/Malixx92 made a cool post that visualises all the building sizes. This is better for people who work well with visual aid! And the comments discuss the undiggable/path placing tiles in front of buildings better than my wonky explanation! e24fc04721