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This Import Plan Outline is intended to help ensure that your "Import plan" document covers as many of the common questions about imports as possible. Just create your own page and cut and paste the wiki text from here (starting from below the line)

Please! If you identify ways that this outline didn't meet the needs of your import (key evidence of this: tons of questions or alarm bells on mailing lists!), please return and fix this page.

Utah Buildings Import is an import of the Utah AGRC dataset which contains building footprints and address information covering sections of the state of Utah. The import is currently (as of 2017-11-09) at the planning stage.


Improve address and building footprint coverage in Utah


2017 - November & December: planning stage

Import Data


The data is sourced from the Utah AGRC, which is a publicly funded governmental dataset. This import focuses primarily on the Building and Address datasets.

Data source site: https://gis.utah.gov/data/location/

Data license: No license (explicit permission given to use to contribute to OSM)

Type of license (if applicable): n/a (No license has been selected yet)

Link to permission (if required): e.g. link to mail list reference url - http://lists.openstreetmap.org/pipermail/imports/2012-December/001617.html

OSM attribution (if required): n/a

ODbL Compliance verified: n/a

OSM Data Files

Link to your source data files that you have prepared for the import - e.g. the .osm files you have derived from the data sources.

Import Type

One time import, done in many separate uploads using a manual process with JOSM and QGIS.

Data Preparation

Data Reduction & Simplification

Describe your plans, if any, to reduce the amount of data you'll need to import.

Examples of this include removing information that is already contained in OSM or simplifying shapefiles.

The dataset will be processed in small subsets of roughly 10-30 city blocks. QGIS is used to extract this subset from the AddressPoints.shp and Buildings.shp files using the following process for each file. Note that the same area should be extracted for both files.

  1. Open the shapefile in QGIS
  2. Select the entities to upload
    1. It is usually easier to orient yourself if you have aerial imagery, available by downloading the OpenLayers plugin for QGIS.
  3. Copy the selected entities (for addresses, this may take a while)
  4. Paste as new vector layer (Edit -> Paste Features As... -> New Vector Layer)
  5. Save this new layer as ESRI shapefile

Then, open both of the new ESRI shapefiles in JOSM using the opendata plugin, by following the instructions here.

Tagging Plans

Describe your plan for mapping source attributes to OSM tags.

Changeset Tags

Describe how you'll use changeset tags in the import.

Changesets will be tagged with the source="Utah AGRC" tag.

Data Transformation

Describe the transformations you'll need to conduct, the tools you're using, and any specific configurations or code that will be used in the transformation.

Data Transformation Results

Post a link to your OSM XML files.

Data Merge Workflow

Team Approach

Describe if you'll be doing this solo or as a team.


List all factors that will be evaluated in the import.


Detail the steps you'll take during the actual import.

Information to include:

  • Step by step instructions
  • Changeset size policy
  • Revert plans

Remove all tags from the Buildings layer. Select all items in this layer, de-select all nodes, and assign all the polygons the tag of building=yes.

Using an aerial imagery underlay that has been calibrated using GPS traces, adjust the building entities so that they match the aerial imagery footprints.

Process the address points layer by converting the following AGRC tags to the relevant OSM tags:

  • AddNum = addr:housenumber
  • City = addr:city (decapitalize)
  • PtLocation = name
  • State = addr:state
  • ZipCode = addr:postcode

Futhermore, keep the following tags to help in processing street/location names:

  • PrefixDir
  • PtType
  • StreetName
  • StreetType
  • SuffixDir

To process the street names, follow the steps below:

  1. For each street in the dataset, find all entities in the layer that have a StreetName matching it. Combine the StreetName and StreetType or SuffixDir tags to create the addr:street OSM tag.
    1. For named streets (e.g. Wilson Ave), the StreetType tag contains the type of street (St, Ave, Cir, etc). For example, for Wilson Ave, StreetName = Wilson and StreetType = Ave. If there are multiple street types for the same name (e.g. Wilson Ave and Wilson Ct), make sure to include the StreetType in your entity query.
    2. For numbered streets (e.g. 700 East), the SuffixDir tag contains the direction. For example, for 700 East, StreetName = 700 and SuffixDir = E.
  2. Assign the selection an addr:street OSM tag according to the tagging above. This will require decapitalizing the street name and fully spelling out the street type or direction.
  3. Once all addresses have had their addr:street tag applied, you can remove the following tags from the dataset
    1. PrefixDir
    2. StreetName
    3. StreetType
    4. SuffixDir

Next, merge the Address and Building layers together.

Using the Buildings JOSM plugin, merge the address points into the building shapes (Data -> Merge Address Points). This will only merge the addresses if there is a single address inside of the building footprint. You will likely run into the following issues:

  1. The address point lies just outside of a building footprint. In this case, just move the address inside of the building footprint and merge the address points again.
  2. A building has 3 address points with the same house number within the same building. Just delete two of these address points and merge the address points again.
  3. A building has 2 address points with different house numbers within the same building. If this is a house, it is likely a duplex. You may choose to split the house into two separate shapes, each with the relevant house number.
  4. A building has many address points within it. This is often seen on apartment buildings. Remove all but a single address points, change the building=yes tag to building=apartments and merge the address points again.
  5. A commercial building has many address points. You may choose to split the commercial building into smaller spaces, or leave the address points unmerged depending on the building configuration. It may be easier to merge the data into any existing OSM nodes if the addresses are not merged into the buildings.

Process the PtType tag using the following guidance:

  1. If the PtType tag is "Commercial", adjust building=yes to building=commercial.
  2. If the PtType tag is "Residential", and the building tag hasn't been adjusted to apartments in the steps above, apply the tag building=house
  3. Review the remaining footprints with building=yes.
    1. There may be some larger public buildings, like churches or libraries, so tag them accordingly.
    2. Some of these may be sheds, in which case apply the building=shed tag.
    3. Most of the remaining entities are garages, so apply the building=garage tag.
  4. Now remove the PtType tag

At this point, do a quick review to ensure that no more AGRC tags are present.


Identify your approach to conflation here.


Add your QA plan here.