Aerial imagery from the National Agricultural Imagery Program (NAIP).
NAIP provides US-wide, high-resolution aerial imagery. This program is administered by the Aerial Field Photography Office (AFPO) within the US Department of Agriculture (USDA). Data are available from 2010 to the present.
Data are stored in cloud-optimized GeoTIFF files in Azure Blob Storage in the West Europe Azure region, in the following blob container:
Within that container, data are organized according to:
More details on these fields:
- Year: Four-digit year. Images are collected in each state every 3-5 years, with any given year containing some (but not all) states. For example, Alabama has data in 2011 and 2013, but not in 2012, while California has data in 2012, but not 2011 or 2013. Esri provides information about NAIP coverage in their interactive NAIP annual coverage map.
- State: Two-letter state code.
- Resolution: String specification of image resolution, which has varied throughout NAIP’s history. Depending on year and state, this may be “050cm”, “060cm”, or “100cm”.
- Quadrangle: USGS quadrangle identifier, specifying a 7.5 minute x 7.5 minute area.
The filename component of the path (m_3008601_ne_16_1_20150804 in this example) is preserved from USDA’s original archive to allow consistent referencing across different copies of NAIP. Minor variation in file naming exists, but filenames are generally formatted as:
m_[quadrangle]_[quarter-quad]_[utm zone]_[resolution]_[capture date].tif
…for example, the above file is in USGS quadrangle 30086, in the NE quarter-quad, which is in UTM zone 16, with 1m resolution, and was captured on 8/4/2014. In some cases, an additional date may be appended to the filename; in these cases, the first date represents the capture date, and the second date represents the date at which a subsequent version of the image was released to allow for a correction. For example:
…was captured on 9/3/2018, and re-released on 2/10/2019. If you’re reading this because you want to digest this filename, the first date is almost definitely what you’re interested in.
Small thumbnails are also available for each image; substitute “.tif” with “.200.jpg” to retrieve the thumbnail. For example, a thumbnail rendering of the image used in the naming convention example above is available at:
A complete Python example of accessing and plotting a NAIP image is available in the notebook provided under “data access”.
We also provide a read-only SAS (shared access signature) token to allow access to NAIP data via, e.g., BlobFuse, which allows you to mount blob containers as drives:
Mounting instructions for Linux are here.
NAIP data can consume hundreds of terabytes, so large-scale processing is best performed in the West Europe Azure data center, where the images are stored. If you are using NAIP data for environmental science applications, consider applying for an AI for Earth grant to support your compute requirements.
A copy of NAIP is also available in the East US Azure region, and will be maintained there until at least the end of 2021, but we encourage users to migrate to the West Europe copy. The East US data is in the following container:
The entire NAIP catalog is also available in a public instance of Esri Image Server. The Image Server base URL is:
An interactive map pulling from this Image Server is available here.
A list of all NAIP files is available here, as a zipped .csv file:
We also maintain an rtree object to facilitate spatial queries for Python users; see the sample notebook for details.
Data can also be browsed here.
Where did the .mrf files go?
In June of 2020, we updated our entire NAIP archive to improve both coverage and maintainability. We also switched from .mrf format to cloud-optimized GeoTIFF, and made some changes to path structures. The .mrf files are temporarily still available in another container; if they are important to your work and you need access, contact
1m-resolution imagery of the area near Microsoft’s Redmond Campus in 2017.
For questions about this dataset, contact
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Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.