Mapillary

Mapillary (mapillary.com ) is a service for sharing geotagged photos developed by a Swedish startup , that was sold to Facebook "Facebook (company)") in June 2020, (now owned by Meta Platforms, Inc. "Meta (company)") ).[[3]]( [[4]]( Its creators want to represent the whole world (not only streets) with photos.[[5]]( [[6]]( They believe that for covering all interesting places in the world, there needs to be an independent, crowdsourced project and a systematic approach to cover interesting areas. Services like Google , having especially equipped cars with camera mounts, are not going to be able to cover the world in sufficient detail.[[5]]( According to them, the local knowledge is almost unbeatable, and people know what really matters in capturing a photo.[[5]]( They are interested in coverage of any outdoor place and this can contribute to a system that represents the world with high level of detail.[[4]]( Most of the "intelligence" of image processing is done on the server side using Big data technologies and computer vision , making the data collection super simple for the user. As a result, Mapillary will improve with each new photo, since all new photos are related to any existing photos in the vicinity. Here is a short presentation [[7]]( for a brief technical overview. The idea is that the users of the data are empowered to increase coverage in areas that interest them.[[5]]( The developers of Mapillary believe there is a place in the market for a provider of neutral and independent pictures.[[5]]( [[6]]( The contributors can install the Mapillary app on WindowsPhone , Android or iPhone , there are successful reports from even Jolla and Blackberry devices that can run Android apps. After registration, the user can start taking photos. In addition to supporting numerous smartphones and action cameras, Mapillary also offers a Mapillary-specific version of the BlackVue DR900 dashcam, the BlackVue DR900M.[[8]]( [[9]]( [[10]]( [[11]]( Mappers can primarily contribute to Mapillary by uploading their captured photo sequences. The most popular methods are either with a smartphone using the Mapillary app which is actively developed for Android and iOS (An open-source Windows app is also available) or from vehicle dashcam or action camera footage/capture uploaded via Desktop Uploader application or mapillary tools command-line scripts. These devices can be used as long as accompanying GPS data is recorded (normally by the device itself, but possibly separately). Photos taken outside the apps can also be uploaded this way. For more information on using Mapillary, take a look at Mapillary/Data collection with Mapillary or Mapillary's help pages . For download, there are several 3rd party options mentioned here , like , mapillary v4 downloader.py or mapillary jpg download.py . (also see Image transfer tool for synchronization between Mapillary and KartaView [defunct in 2023]). Mapillary uses computer vision to recognize map features (objects) from the images, ranging from traffic signs to more experimental object and line recognition. The experimental features are based on semantic segmentation. While serving as a useful visual aid for OSM editing, Mapillary also processes photos contributed using computer vision. Every photo is processed with identified faces and license plates blurred. Traffic signs convey important information about road restrictions and junction layouts, and are mapped on OpenStreetMap using the traffic sign = key. Mapillary first introduced automatic traffic sign recognition in January 2015 , and about a month later launched a system for manual validation of these recognition results, in the form of a game (currently doesn't work). This feedback led to improved automated recognition results , which were further improved by using country-specific models , and later worldwide appearance groups to cover more countries using a single model. These appearance groups are documented here . A full list of suppor