Mapillary imagery and data are available for enhancing OpenStreetMap via both JOSM and iD editor, the main tools for editing the map. The community uses Mapillary imagery as ground-level reference for adding attributes related to roads, buildings, amenities, parks, traffic signs, and much more.

HERE partners with Mapillary to support community mapping efforts to improve HERE maps. Mapillary imagery is available in the Map Creator editing tool for making improvements to HERE maps. Millions of people using apps powered by HERE maps thus have access to richer, fresher location data.

Mapillary integrations across the ArcGIS suite bring street-level imagery to Esri maps. As the leading GIS software provider, Esri products are used by GIS professionals across industries, from government and commercial sectors to conservation and disaster reponse.

To serve a big customer in the logistics sector with fast map updates, Kaart requested imagery capture in Melbourne, Australia, through the Mapillary Marketplace. While a local network of qualified drivers was working on imagery capture, Kaart had access to fresh imagery continuously and was able to focus on making map updates in OpenStreetMap, using street-level imagery as a reference to add information about turn restrictions, street names, and speed limits.

A&B Courier equipped their fleet with Mapillary Dashcams to collect street-level imagery from the fleet's routes. The delivery company uses the imagery as reference to give more detail when reporting map issues to their map provider. The map provider, in turn, uses the imagery when updating the map, so that it reflects real-life conditions and provides accurate routing data for planning the fleet's operations.

The Mapillary plugin in the OsmAnd app enables street-level views for routing and navigation. App users can easily check out the surroundings at any place of interest or along the route they have planned with OsmAnd. Where imagery is missing, contributors can add it themselves to help others navigate better.

The OpenStreetMap Navigation map, developed by MapBox, is a web application which uses Mapillary vector layer to highlight the traffic signs detected in Mapillary images. The detections are used to add, verify, and edit turn restrictions on OpenStreetMap.

Connecticut DOT has been using photologs to get a visual reference of places, for example when doing road safety works and asset inventories. The need to have a low-cost method for updating imagery and see it quickly after capturing brought them to Mapillary where they can host all their photologs for free and update them as often as needed, with imagery and data available already within a day. In addition, it is now easy for the DOT to share the imagery with the partners such as universities and regional planning organizations that they work with on collaborative projects.

The District of Columbia Department of Public Works uses Mapillary to monitor and optimize the process of leaf clean-up from the streets of Washington, D.C. Collecting imagery of the leaf cleaning crew's activity before and after cleanup sessions, they're able to document the progress and use it as input for improving their leaf-collection strategies. The imagery is available both on the Mapillary platform as well as in the city's ArcGIS tools in a convenient before-and-after comparison feature to make the leaf-cleaning process more transparent to everyone, including public works employees, the city's management, and residents.

The City of Clovis has been using Mapillary to do their first-ever traffic sign inventory. With only one employee in the GIS department, who also has many other projects to take care of, it was challenging to find the budget and time resource to conduct the inventory. Mapillary enabled their GIS expert to collect street-level imagery himself and use the automatically extracted traffic sign features as a base for the inventory, slashing the required budget and time.

The City of Detroit uses Mapillary to get a good overview of street and road assets in the fast-changing city. Using a high-resolution 360-degree camera, their GIS team collected 1.5 million images that are shared between the city's 30+ departments and also available publicly to everyone through the Mapillary platform. Using Mapillary's automatic feature extraction, city officials can speed up asset management tasks and proactively improve the residents' lives through everything from safety to insurance rates to snowplowing.

Märker Geospatial provides roadway management services across the US. Their projects often include collecting right-of-way photologs which help save their customers' time and money by reducing the need for costly field visits during inventories and inspections. Märker Geospatial captures imagery with phones, action cameras, 360° cameras, or professional rigs, depending on each customer's needs. Mapillary enables them to collect and process images from these different sources, providing visual reference as well as automatic data extraction from the imagery.

Los Angeles County Department of Public Works uses Mapillary for quick inventory of 30,000 traffic signs on unincorporated county roads. Traffic inspectors drive out with action cameras mounted on their vehicles when a new capture or a refresh is needed. Upon upload to Mapillary, the imagery is available to view in ArcGIS and the automatically extracted traffic signs are provisioned as shapefiles.

The Erie County OH Auditor uses Mapillary for virtual site visits as part of the assessment workflow. The auditor collects imagery with four Sony action cams mounted multi-directionally on the vehicle to collect different vantage points. Upon upload to Mapillary, the imagery is available in Bruce Harris' Paperless Reappraisal System, to complement the county's oblique imagery.

Lithuanian Road Administration (LRA) partnered with the nonprofit Road and Transport Research Institute to capture 360° imagery across a road network spanning 21,244 kilometers using the Ladybug 5 camera system. The 4 TB photolog was uploaded to Mapillary for cloud hosting, enabling public distribution as open data, lower storage costs, and easy integration into other applications. The project is intended to give the most up-to-date visual information about Lithuanian highways and road assets, as well as to support future planning efforts.

Vermont Department of Transportation (VTrans) hosted 5 years of road imagery on Mapillary as open data. 5 million images are now available to the public as well as for VTrans to use in their work. Mapillary offers them a delightful viewing experience, integration into their key workflows (e.g. traffic sign inspection, pavement maintenance), and automated feature extraction for surveys and inventories.

The City of Amsterdam captured and released 800,000 panoramic images of the city as part of their City Data open data initiative. Within a week of the release, the 10 TB dataset of imagery was uploaded to Mapillary, and processed to extract images containing street signs, street lights, crosswalks, trash cans, and more.

Bike Ottawa, the leading citizen group for bike safety in Ottawa, captured 450,000 images across 2,000 km in a little over a month to gather data for bike advocacy. The imagery is being used to determine the need for infrastructure creation and maintenance, build a model for traffic stress levels, and monitor developments over time.

The City of Johns Creek GA implemented Mapillary as a cost-effective tool for collecting street-level imagery to improve access to open data. With help of citizens, they were able to collect and maintain comprehensive location data for public and private assets on city streets. This gave the city staff access to recent geospatial data across the ArcGIS platform with the opportunity to keep the imagery current with frequent updates.

Project for Public Spaces, the NYC-based NGO leading the placemaking movement, worked with local partners in Philadelphia to transform the City Hall courtyard into a thriving showcase for the up-and-coming arts scene. PPS gathered input from local artists for designing an actual stage as a performance venue, and added sustainability features throughout the space.

The Red Cross partners with Humanitarian OpenStreetMap and Doctors Without Borders on Missing Maps, a humanitarian project that maps parts of the world vulnerable to natural disasters, conflicts, and disease epidemics. Mapillary has collaborated with Missing Maps on capture efforts in Haiti, West Africa, and Bangladesh. The Red Cross was also one of the first organizations on the ground in Dominica in the aftermath of Hurricane Maria, capturing valuable imagery for delivering aid and assessing damage.

The World Bank, the Global Facility for Disaster Reduction and Recovery (GFDRR), the Red Cross, and the Humanitarian OpenStreetMap Team support Dar Ramani Huria, a community mapping project in Dar es Salaam, Tanzania. In a city facing rapid growth, congestion, and bi-annual flooding, the lack of maps for developing resilient infrastructure is a critical problem.

Swiss Federal Railways (SBB), in partnership with MyHandicap, launched a public website (ViaStaziun.ch) to display crowdsourced Mapillary imagery of train stations in Switzerland. The goal of the website is to provide visuals of station features including stairs, construction work, barriers, and other hurdles that may affect how people with reduced mobility travel. SBB used Mapillary for hosting the imagery to encourage public sharing of imagery to drive crowdsourcing.

The World Bank Transportation and ICT group works with local contractors in Southwestern China to capture imagery for a rural accessibility project. The contractors are required to use the Mapillary app as they drive the road network, so the World Bank team in Washington DC can evaluate the environment and road conditions remotely. As development work progresses and more images are uploaded, the World Bank can use Mapillary’s comparison feature to observe changes over time.

Autonomous Intelligent Driving (AID), an Audi subsidiary, licensed the Mapillary Vistas Dataset to develop technology for self-driving cars across the whole Volkswagen Group. They are using the Vistas to improve the Deep Neural Networks for perception and prediction. With images from all over the world and from different kinds of cameras, the dataset is ideal for improving the robustness of AID’s models, and for verifying their performance in applications on potential markets all over the world.

Toyota Research Institute (TRI) develops state-of-the-art machine learning algorithms for autonomous driving to realize safe and accessible mobility for the future. They use the Mapillary Vistas Dataset as a benchmark for their algorithms for understanding driving scenes. Vistas help accelerate the deployment of high-standard algorithms that are robust to the diversity of real-world data, making it possible to put novel deep learning algorithms into actual products.