OSM Fieldwork

Processing field data from OpenDataKit to OpenStreetMap format, and other field utils.

📖 Documentation: https://hotosm.github.io/osm-fieldwork/

🖥️ Source Code: https://github.com/hotosm/osm-fieldwork

History 📖

The History of the OSM Fieldwork Project begins with Rob Savoye, Senior Technical Lead at Humanitarian OpenStreetMap Team.

In 2010, Rob's rural volunteer fire department(he is also a long time free software developer for the GNU project, fire-fighter, climber, disaster tech support.) faced the challenge of outdated giant paper mapbooks with incomplete information. Despite Google having limited address and remote road coverage, the lack of cell service made it impossible to rely on it for verification. Determined to find a solution, Rob turned to OpenStreetMap (OSM). His first step involved importing building footprints and addresses into OSM, greatly aiding the fire department in locating places quickly and easily. The response time was significantly reduced, nearly halved. Given that most of the roads were dirt jeep trails, Rob undertook ground-truthing the highway and trail data in OSM. Over the course of several years, he diligently added precise information about all the highways in his area, enabling the fire department to determine the appropriate response vehicles for each scenario. Once Rob had successfully improved the fire district maps, he expanded his efforts to map the remote regions of Colorado and a few neighboring states, proving invaluable during large wildland fires. Ground-truthing became an integral part of his work, conducted using mobile devices in the field. To streamline the data collection process, Rob heavily relied on OpenDataKit and eventually created additional software to facilitate data processing, which had previously been time-consuming and tedious. Now, transferring data seamlessly from his phone to OSM requires minimal effort. To this day, Rob continues his weekly field mapping every few months while continuously enhancing the software used in the project.

About OSM Fieldwork

Osm-Fieldwork is a project for processing data collection using OpenDataKit into OpenStreetMap format. It includes several utility programs that automate part of the data flow like creating satellite imagery basemaps and data extracts from OpenStreetMap so they can be used with ODK Collect. Many of these steps are currently a manual process. All of the programs in osm-fieldwork are designed to function as the backend of a webpage, but to also work standalone and offline. The standalone functionality are simple command line programs run in a terminal. They were originally created for producing emergency response maps in the Western United States, which is explained in this talk from SOTM-US 2022 titled OSM For Firefighting. Much of the tech and usage is explained in these tech briefs. Currently these are now part of the backend for the Field Mapping Tasking Manager project at HOT.

Installation

To install osm-fieldwork, you can use pip. Here are two options:

• Directly from the main branch:

pip install git+https://github.com/hotosm/osm-fieldwork.git

• Latest on PyPi:

pip install osm-fieldwork

Configure

Osm-Fieldwork can be configured using a simple config ($HOME/.osm-fieldwork)file in your home directory, or using environment variables.

Config file

The config file is used to store the credentials to access an ODK Central server. You must have an account on the Central server of course for this to work. That file looks like this:

url=https://foo.org
user=foo@bar.org
passwd=arfood

Environment Variables

• LOG_LEVEL

If present, will change the log level. Defaults to DEBUG.

• ODK_CENTRAL_URL

The URL for an ODKCentral server to connect to.

• ODK_CENTRAL_USER

The user for ODKCentral.

• ODK_CENTRAL_PASSWD

The password for ODKCentral.

• ODK_CENTRAL_SECURE

If set to False, will allow insecure connections to the ODKCentral API. Else defaults to True.

Using the Container Image

• osm-fieldwork scripts can be used via the pre-built container images.
• These images come with all dependencies bundled, so are simple to run.

Run a specific command:

docker run --rm -v $PWD:/data ghcr.io/hotosm/osm-fieldwork:latest json2osm <flags>

Run interactively (to use multiple commands):

docker run --rm -it -v $PWD:/data ghcr.io/hotosm/osm-fieldwork:latest

Note: the output directory should always be /data/... to persist data.

Utility Programs

These programs are more fully documented in this file. This is just a short overview.

CSVDump.py

This program converts the data collected from ODK Collect into the proper OpenStreetMap tagging schema. The conversion is controlled by an YAML file, so easy to modify for other projects. The output are two files, one is suitable for OSM,and is in OSM XML format. The other No converted data should ever be uploaded to OSM without validating the conversion in JOSM. To do efficient conversion from ODK to OSM, it's best to use the XLSForm library as templates, as everything is designed to work together.

basemapper.py

This program creates basemaps of satellite imagery, and produces files in mbtiles format for ODK Collect and sqlitedb files for Osmand. Imagery basemaps are very useful when the map data is lacking.or in ODK Collect, selecting the current location instead of where you are standing. The basemaps Osmand are very useful of navigation where the map data is lacking. Imagery can be downloaded from ERSI, Bing, USGS Topo maps, or Open Aerial Map

make_data_extract.py

This program makes data extracts from OpenStreetMap data. Multiple input sources are supported, a local postgresql database, or the HOT maintained Underpass database.

json2osm

odk2csv.py, odk2geojson.py, odk2osm.py

These programs ER used when working offline for extended periods. This converts the ODK XML format on your mobile device into the same CSV format used for submissions downloaded from ODK Central, or the JSON format also from Central.

odk_merge.py

This program conflates the data collected by ODK Collect with the existing OSM data. The output of this file can be loaded into JOSM for validation and uploading to OpenStreetMap.

odk_client.py

This program is a simple command line client to an ODK Central server. This allows you to list projects, appusers, tasks, and submissions. You can also delete projects, tasks, and appusers, but this should only be used by developers as it does direct database access, and you could lose all your data.

filter_data.py

This program is used to support humanitariam data models. It extracts the tags and values from the data models document developed by HOT, and compares those to the taginfo database to help fine tune what data goes into OSM or the private output data. This is to not flood OSM with obscure tags that aren't supported by the community. It also filters data extracts so they work with ODK Collect.

osm2favorites.py

This is a silly program, but it takes a GeoJson file, usually an OSM data extract and generates a GPX file with styling for OsmAnd. This is useful when ground-truthing map data, as it can be used for navigating to those areas.

Best Practices and troubleshooting

To ensure the quality of your converted data, here are some best practices to follow:

• Always validate your conversion in JOSM before uploading to OpenStreetMap.

• Use the XLSForm library as templates to ensure that your ODK Collect data is compatible with the conversion process.

• If you're having trouble with the conversion process, try using the utility programs included with Osm-Fieldwork to troubleshoot common issues.

For more info visit the troubleshooting page.

By following these best practices and using the utility programs included with Osm-Fieldwork, you can effectively process data collection from OpenDataKit into OpenStreetMap format. However, please note that while Osm-Fieldwork has been tested and used in various projects, it is still in active development and may have limitations or issues that need to be resolved.

XLSForm library

In the XForms directory is a collection of XLSForms that support the new HOT data models for humanitarian data collection. These cover many categories like healthcare, waterpoints, waste distribution, etc... All of these XLSForms are designed to have an efficient mapper data flow, edit existing OSM data, and support the data models.

The data models specify the preferred tag values for each data item, with a goal of both tag completeness and tag correctness. Each data item is broken down into a basic and extended survey questions when appropriate.

What is an XLSForm?

An XLSForm is a spreadsheet-based form design tool that allows you to create complex forms for data collection using a simple and intuitive user interface. With XLSForms, you can easily design and test forms on your computer, then deploy them to mobile devices for data collection using ODK Collect or other data collection tools. XLSForms use a simple and structured format, making it easy for you to share and collaborate on form designs with your team or other organizations.

Using the XLSForm Library with Osm-Fieldwork

The XLSForms in the XForms directory of the XLSForm Library have been designed to support the HOT data models and have an efficient mapper data flow. These forms also allow for editing of existing OSM data and support the data models, specifying the preferred tag values for each data item with the goal of both tag completeness and tag correctness.

Here are some examples of how to use the XLSForm Library with Osm-Fieldwork

• Download an XLSForm from the XForms directory:

wget https://github.com/hotosm/xlsform/raw/master/XForms/buildings.xls

• Convert the XForm to OSM XML using CSVDump:

• Use the resulting OSM XML file with JOSM or other OSM editors to validate and edit the data before uploading it to OpenStreetMap.

Summary

The XLSForm Library is a valuable resource for organizations involved in humanitarian data collection, as it provides a collection of pre-designed forms that are optimized for efficient mapper data flow and tag completeness/correctness. By using the XLSForm Library with Osm-Fieldwork, you can streamline your data collection process and ensure the quality of your data.

Osm-Fieldwork is a powerful tool for processing data collection from OpenDataKit into OpenStreetMap format. By following the best practices outlined in this documentation and using the utility programs included with Osm-Fieldwork, you can streamline your data collection process and ensure the quality of your converted data. If you have any questions or issues with osm-fieldwork, please consult the project's documentation or seek support from the project's community.