Machine learning

Imports and automated edits should only be carried out by those with experience and understanding of the way the OpenStreetMap community creates maps, and only with careful planning and consultation with the local community.
See Import/Guidelines and Automated Edits code of conduct for more information. Imports/automated edits which do not follow these guidelines might be reverted!

 Machine Learning is a subdomain of  Artificial Intelligence centered around the idea that machines can be trained to recognize patterns based on annotated input data. In the context of maps and OSM, a common application is to annotate a sample set of imagery (aerial or street-level) in order to automatically derive information and/or map features from a larger dataset. By the types of features it looks at, specific tasks can be:

• Road extraction (sometimes takes GPX too!)
• Building segmentation (footprints)
• land cover classification, for landuse tagging
• Other types of object detection.

As with many areas of computer vision, competitions are sometimes held to assess and encourage better models to be made. One example is DeepGlobe 2018.

Applications

This is a list of existing applications of Machine Learning designed to support OSM.

Open Source

Name	Description	Year	Organisation	External Links
Looking glass	Tool to identify buildings within satellite imagery. It uses a segmentation algorithm to label each individual pixels in an image as to whether it's part of a building or not.	2018	Development Seed	Source code
Mapping the Electric Grid	Using machine learning to augment human tracing of high-voltage infrastructure	2018	Development Seed	Documentation and Source code
MapSwipe ML	Tools for user input from MapSwipe as input for ML applications. More specifically, judging whether an area is build up.	2017	Missing Maps	Source code
ml-enabler	A registry and facilitator for machine learning models for the OpenStreetMap ecosystem.	2019	Humanitarian OpenStreetMap Team and Development Seed	Blog post and source code
OSM Analytics Gap Analysis	Compares OSM data with machine learning derived prediction of built up areas to show where gaps in OSM data may exist.	2018	Humanitarian OpenStreetMap Team	Source code and data set
RapiD	An extension of the iD editor for mapping with AI-generated features.	2019	Facebook	Website, source code, documentation
RoadTracer	Automatic extraction of road networks from aerial images.	2018	MIT CSAIL	Documentation and source code
Robosat	Generic ecosystem for feature extraction from aerial and satellite imagery.	2018	Mapbox	Source code
Telenav.AI	Sign detection from street level imagery	2018	OpenStreetCam (Telenav)	Blog post Source code

The following project uses other fields of artificial intelligence (finite state machines) rather than machine learning techniques:

Name	Description	Year	Organisation	External Links
Trip Simulator	A tool for generating simulated raw GPS location telemetry.	2019	SharedStreets	Source code

Proprietary Software

Name	Description	Year	Organisation	External Links
Recognize and Label Objects in the Wild	Object and feature detection from street level imagery (usable from iD)	2016	Mapillary	Blog post
AI-Assisted Road Tracing	Road detection from aerial imagery and making it available to the mapper through the RapiD editor.	2016	Facebook	Wiki page
US building footprints	Building footprints detected from ML	2018	Microsoft	Blog post and data
Urchn	Urchn is a tool for urban change detection. It shows you how a city is changing, and helps to keep the map up-to-date.	2018	Development Seed and Radiant Solutions	Blog post

Relevant Training Data

• https://github.com/greatea/Telenav.AI offers "45 000 manually annotated images containing more than 55 000 signs divided in 23 different classes". A sample dataset with 1000 images is also available.
• Mapbox allows all users to use Mapbox Satellite images for machine learning processes

Conference talks and presentations

• The Machine Mappers are Coming (Mapbox, SOTM US 2018)
• OSM at Facebook (Facebook, SOTM US 2018)
• OpenStreetCam Is Now An Open Machine Learning Platform for OpenStreetMap (Telenav, SOTM US 2018)
• Analytic Support for Contributors: Defining Levels of Automation for Machine Learning Applied to Crowdsourced Mapping (SpaceNet, SOTM US 2018)
• Semi-Automated Map Editing (MIT CSAIL, SOTM US 2018)
• Urchn Tells You Where Cities Change, and Where OSM is Out-of-date (Development Seed, SOTM US 2018)
• How Deep Learning could help to improve OSM Data Quality? (DataPink, SOTM 2018)

See also

• Natural language processing
• Machine learning OSM mailing list