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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.
This is a list of existing applications of Machine Learning designed to support OSM.
|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||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:
|Trip Simulator||A tool for generating simulated raw GPS location telemetry.||2019||SharedStreets||Source code|
|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||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)
- ↑ Pratik Yadav (18 May 2018). “Mapbox Satellite for Machine Learning”. “All Mapbox users, using an access token from their own account, are allowed to create derivatives from Mapbox Satellite for contribution to OpenStreetMap via manual or automated processes for free.”