Besides making street-level imagery available to anyone, Mapillary also uses computer vision to automatically extract map data from images. We categorize our map data in two main ways:
Object detections are instances of different objects that have been detected in images. Since Mapillary images are geotagged, you can get a dataset of image locations for a particular object that interests you, and use that as a filter to quickly find and look at all images where that object is present.
Example of object detections on the Mapillary web app
| To learn more about object detections, how we create them, and how you can use them, see our article on object detections. |
Map features entail different objects positioned on the map. If the same object has been detected in multiple geotagged images, we can use triangulation to estimate the location of the object and position it with a latitude and longitude.
We split map features into two categories: points and traffic signs. Mapillary currently supports 42 point features and 1,500+ classes of traffic signs. Due to the sheer amount of traffic sign types, and a need to differentiate between them, we like to think of traffic signs as their own category when referring to map features.
Example of traffic sign map features on the Mapillary web app
| To learn more about map features, how they're generated, how you can explore them, and how you can integrate them into your workflows, see our article on map features. |
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