Mapbox Drive is the first lane guidance map designed for car companies to control the in-car experience. Built specifically for semi-autonomous driving, ride sharing, and connected cars, the map is updated live from our network of sensors. From designing the map to customizing the navigation, we're building the platform for you to own every aspect of the relationship with the driver.

We're turning phones, cars, and other devices into a network of real-time sensors. Our Mobile SDK collects billions of points that let us see every street, analyze the speed of traffic, and connect the entire road network. Last week alone we collected and processed over 100 million miles of sensor data into our maps. Anonymized and aggregated data preserves privacy while allowing us to quickly detect road changes.

We've built the data processing pipeline to filter our fire hose of sensors into accurate traffic, directions, and lane guidance maps. The updated map is delivered over the air to the connected car, using our lane trajectories like rails built into the road, guiding the vehicle and smoothly changing lanes.

Customizable design

Using Mapbox Studio you can now customize the dash experience.

Every map is designed perfectly for each in-car hardware display, from the cockpit to the console. Using video game technology, the driver experiences the map at 60 frames per second, just like maps feel on a mobile phone.

A flexible and fast platform

Mapbox Drive is a platform for you to design the dashboard for the driver and own the data from your fleet. The platform can power traffic-aware turn-by-turn navigation, guide drivers with beautiful maps, find destinations with instant search, integrate seamlessly with mobile devices, and enable semi-autonomous driving.

Directions: Turn-by-turn text and voice guidance. Routing tuned to real-world traffic conditions based on mobile telemetry data. Real-time coverage of over tens of million miles of road per day.

Maps: Beautiful streets and satellite maps with global coverage. Latest traffic and terrain with your custom data layers. Live updates to map and traffic data using super lightweight vector maps for fast online and offline rendering.

Mobile: Seamless integration of mobile map design and routing behavior with dashboard maps.

Own your data: Analyze fleet data to improve your drivers' experience and build your own data.

Search: Addresses and points of interest around the world. Responsive type-ahead geocoding for fast routing and guidance assistance.

Autonomous: Road and lane centerlines, built from telemetry data to improve guidance and support autonomous and semi-autonomous driving.

Open Source

We’re building this all in the open on Github, from our iOS and Android SDKs, to Mapbox GL Native Qt support, vector tile specifications, and our OSRM routing engine. We’re also improving open data in OpenStreetMap.

We regularly use Notes on OpenStreetMap to share and solve problems on the map. With Notes, anyone can quickly point out errors or omissions in the map and ask for verification from local contributors.

Whether you are a new user or a power mapper, here are tips to leverage the power of Notes on OpenStreetMap.

Help resolve Notes

To get started, take a look at open Notes in an area familiar to you. Go to OpenStreetMap.org and enable the Map Notes overlay, and you will see unresolved Notes in your area.

Finding notes on OpenStreetMap.

Red markers are currently unresolved Notes. Click a marker to display the details of the Note on the left panel. If you can fix the problem, edit the data using your favorite OpenStreetMap editor. Once you resolve a Note, add a comment and mark Comment & Resolve. The marker will turn green to show that it has resolved.

Resolve notes in your local area.

Add a Note

When you are unable to reliably verify a problem based on Bing or Mapbox Satellite imagery or other sources, or are unsure how to fully map the feature, add a Note to discuss the feature in question with the community. Mappers with local knowledge in the area can review your Note, and make sure that OpenStreetMap data matches the on-the-ground reality.

Add a note on OpenStreetMap - you can also do this using the JOSM editor.

Click the Add a note to the map icon. Move the marker precisely to the location of concern, and type up a relevant comment or question that explains the issue. Give other mappers a clear understanding of the problem, so they can take action and provide the best possible update to the map.

Now that you are familiar with Notes, try it out, and help improve the data on OpenStreetMap. If you are new to the OpenStreetMap project, take a look at our OpenStreetMap 101 series to learn how to get started.

Hit me up on Twitter for any questions or for sharing interesting stories related to your Notes.

We’re thrilled to welcome Maanya Umashaanker to the data team! Maanya joins us to improve OpenStreetMap and other datasets using satellite imagery, probe data, and analyzing customer feedback.

Maanya holds a master’s degree in Geo-informatics from Visvesvaraya Technological University. Before joining Mapbox, she worked as a Senior Research Fellow at the Indian Institute of Science in Bangalore studying the evolution of glacial lakes in the Indian Himalayas. She has also worked as a Project Associate at IIT Madras to optimize power consumption and improve the battery backup of Aakash Tablets.

Welcome Maanya!

We are excited to welcome Manohar to Mapbox. Manohar joins us to improve OpenStreetMap and other datasets using a combination of satellite imagery and analysing customer feedback.

Manohar studied Geoinformatics at Andhra University, and later graduated from Universität Stuttgart with a master’s degree in Geomatics Engineering. As part of his thesis, he worked on developing content-based image retrieval methods.

Welcome Manohar!

The annual Esri User Conference is coming up at the end of the month, and we can’t wait to see you there. We’ll be hanging out on the USS Midway, one of the longest-serving aircraft carriers in the US Navy. It’s now berthed in San Diego and home to 29 restored aircrafts. Join us on Tuesday, June 28th at 7 pm on the flight deck for drinks, live music, and a proper San Diego fish taco.

We’ll also be in the area Monday, June 27th — Wednesday, June 29th meeting with partners and talking about a new set of features that easily connect the Mapbox and ArcGIS platforms.

If you are in San Diego and want to meet, shoot me an email at hannah@mapbox.com. See you there!

The latest Mapbox GL JS release (v0.19.0) includes data-driven styling support for fill-color and fill-outline-color. Data-driven styling opens up advanced data vizualization and map design techniques.

Below is a choropleth map that uses fill-color to visualize median income in every county in the United States. Notice how smoothly the map moves, how the map responds visually to mouse hovering, and how the sidebar is populated with map data.

The data visualization uses property functions to assign a fill-color value to an area based on a particular data value:

'fill-color':{property:'median-income',stops:[[20000,'#fff'],[120000,'#f00']]}

Check out this Mapbox GL JS example to see property functions for fill-color in action.

Make a map!

We would love to see the maps you’re building with data-driven styling! If you have something to share, tweet at @Mapbox.

Xinnong Yang is joining Mapbox to lead business development in China. Based in Shanghai he will work on expanding our sales and partnerships with Chinese and international customers. Xinnong comes with years of experience building customer relationships and implementing complex location based projects with Versalinks, Autodesk, Trimble, Esri, and deCarta.

Get in touch with Xinnong!

杨莘农先生将任职Mapbox中国区业务开发总监。位于上海，他将致力于开拓Mapbox在中国的销售市场和发展与国际客户的合作伙伴关系。杨莘农先生有多年建立客户关系和开发LBS等大型复杂系统平台的经历。他曾在ESRI，Autodesk，Trimble，deCarta等公司担任重要职务。

联系杨莘农先生。

Angelina Calderon has joined the Mapbox team in San Francisco! As a member of our customer success team, Angelina helps Mapbox customers get the most out of the platform.

Prior to joining Mapbox, Angelina helped startups grow through strategic recruiting at The Sourcery. Welcome to the team, Angelina!

Brazil’s preparations for this summer’s Olympic Games in Rio de Janeiro have taken six years, $11.1 billion, and transformed parts of the city. High-resolution satellite imagery from DigitalGlobe is now available in Mapbox Satellite and it reveals venues under construction, neighborhoods reshaped, and environmental impacts from depolluted waterways to a golf course carved from a nature preserve.

Click through the map below to take an Olympic tour.

The imagery, building footprints, and other data layers are available as part of DigitalGlobe’s Rio Olympics package. Follow @DigitalGlobe to learn more. Stay tuned for more Olympic maps from Mapbox, and tweet at me for tips on combining imagery and vector data with Mapbox Studio and Mapbox GL JS.

Last week’s launch of our redesigned maps included a fresh revamp of Mapbox Satellite Streets. Our goal was to add meaningful context of vector data to our vibrant Mapbox Satellite imagery without sacrificing legibility to create a navigation-friendly map.

To maintain legibility over disparate terrain tonalities, we defined a hierarchical color palette that emphasizes major connections. We then deliberately reveal and dissolve this emphasis, simultaneously with the progression of zoom. This results in purposeful navigation information when you need it with fewer distractions in the map.

Our redesigned highway shields and expanded coverage help to distinguish major and minor thoroughfares as you navigate from rural to urban landscapes. Our updated transit icons add another level of information and remain legible upon higher zooms.

At the street-level, we established roads with a subtle tint as paths for biking, walking, and hiking begin to come into focus and further define cityscapes. We accentuated place and road labels by increasing contrast which pulls them into the foreground for a faster read.

Finally, the updated landmarks orient users and make the map more navigable from the street level. We designed a comprehensive set of simplified, modern POIs that create visual space between these acclimating features and the imagery.

Explore Mapbox Satellite Streets further, and start building with it today!

A big welcome to Ajith Ranka, who has joined the Mapbox team! Ajith will focus on improving OpenStreetMap data by building better tools for mapping and validation.

Ajith is passionate about good design, programming and data. Prior to joining Mapbox, Ajith worked his way through Geekskool, an intensive programming bootcamp, where he contributed to frpjs, swipeview and jslisp.

Welcome, Ajith!

Hannah Judge has joined the Mapbox customer success team in San Francisco! Hannah uses her experience in mapping to help our customers get the most from our maps and APIs.

Prior to joining Mapbox, Hannah mapped community health projects in Liberia. Welcome to the team, Hannah!

The OpenStreetMap community and database are moving so fast, it is hard to track all the growth. Humanitarian OpenStreetMap Team and Missing Maps have recently launched new tools to understand the progress of local communities, projects and individuals. Mapbox has been happy to be part of the development conversations, and we want to share them with you. These are incredible resources for all OpenStreetMap communities to find opportunities, focus resources, build relationships and scale our network.

OSM Analytics

With OSM Analytics, you can interactively analyze and visualize growth of the map and the user community in a particular location. The tools can chart the number of roads and buildings added over time, and visualize changes before and after events. This has been a key help for tracking progress in the Ecuador Earthquake Response.

OSM Analytics is built on OSM QA Tiles, and has room to grow to other kinds of features and deeper analysis. Try it out on osm-analytics.org, and contribute to development on GitHub.

OSM Stats

Our friends at Development Seed built OSM Stats for the new Missing Maps Profiles and Leaderboards. It tracks contributions to specific mapping projects and events in real time, with an architecture designed to listen to the high volume of data distributed from OpenStreetMap. Compete against your fellow mappers, earn badges, and visualize exactly where and when you’ve contributed. Find out more and contribute to the OSM Stats GitHub project.

How could you use these tools, and what more can they do to build OpenStreetMap community? We would love to hear your ideas!

We released new Mapbox Light and Dark maps last week as a part of our broader map redesign and relaunch. We have offered updates to Light and Dark in the past, but this iteration presented an opportunity to make larger changes to the pair.

Though much has changed about these maps, our fundamental goals for Light and Dark are not different from past versions: each remains a versatile map designed to complement a wide variety of visualizations and overlays. Thinking how we could improve the maps on these terms, however, has led to significant changes.

Feeling that the maps could provide better context to overlaid content, we have added data back into these versions of the map. Light and Dark benefit from the new data featured in all our redesigned maps, but we also returned data that had been previously removed. These are the most data dense versions of Mapbox Light and Dark we have ever offered, and we are excited to see how they add a new level of context to your projects.

We have also completely overhauled the color systems for both Light and Dark. Just as we knew these maps would benefit from additional data, we also wanted to use color to introduce a greater sense of depth. As their names suggest, these maps operate within relatively constrained color spaces, but we developed a palette of subtly saturated light and dark tones to differentiate land use, land cover, water, and building layers. The differences between each are subtle – overlaid data still stand out – but even slight variations in saturation and hue improve the legibility of each element and provide a richer context.

We approached Light and Dark’s redesign looking to bolster their role as underlays by adding more visual information and depth without distracting from their core mission. Explore Mapbox them both further, and start building with Light and Dark today!

For developers who have experience building web maps with Mapbox.js or Leaflet, switching old projects to use Mapbox GL JS can drastically improve the performance of your existing applications. Mapbox GL JS uses WebGL client-side rendering to display your maps, which results in faster loading, smoother transitions when zooming or panning, and greater flexibility to change map data and styles on the fly. These improvements make switching to Mapbox GL JS well worth the effort, so I recently converted Peter’sCourier demo from Mapbox.js to GL.

Here are some key tips to illustrate how you can make the switch with your projects!
### Adding map layers and data

In the Courier demo, the pickup points for packages are stored as GeoJSON in the browser and constantly updated with new orders. In Mapbox.js, we added those points to the map using L.mapbox.featureLayer(), and with each update of the pickupPoints GeoJSON object we update the layer’s source using featureLayer().setGeoJSON():

varpickups=L.mapbox.featureLayer(pickupPoints).addTo(map);// when new pickups are addedpickups.setGeoJSON(pickupPoints);

In Mapbox GL JS, we first add a source to the map, and then create a new layer:

map.addSource('pickups',{type:'geojson',data:pickupPoints});map.addLayer({id:'pickups',source:'pickups',type:'symbol',layout:{'icon-image':'pickup'}});

When new data are added to the pickupPoints object, we update the new layer’s underlying source using setData() instead of updating the layer directly:

map.getSource('pickups').setData(pickupPoints);

Adding custom markers to Mapbox GL Maps

Because feature layers in Mapbox.js are DOM elements, it is possible to use any image file as a marker on the map. In Mapbox GL JS, however, markers are rendered by the GPU, not the browser, so any images you would like to use as markers have to be loaded into a sprite and referenced in your map’s style JSON.

You can add custom markers to a sprite in Mapbox Studio - this handy guide walks you through the whole process. Alternatively, you can use spritezero, our open-source sprite generator to build your sprite from the command line, or on the fly in your application.

Animations in Mapbox GL JS

We can animate DOM elements using CSS, which is how Peter animated the line of the couriers’ paths to their destinations in the original Courier demo. Since we cannot use DOM manipulation, we’ll use the same setData() method on our GeoJSON source that we used in the above pickupPoints example, but this time we’ll use it in conjunction with Turf and the JavaScript method window.requestAnimationFrame just like this example.

functionanimateCourier(route,destination){// set animation speed at 60x real timevarspeed=60;varduration=route.duration;varpath=turf.linestring(route.geometry.coordinates);vardistance=turf.lineDistance(path);// get starting time of the animationvarstart=performance.now();functionanimate(timestamp){varcurrent_time=timestamp-start;// if the animation has reached its destination, // move the courier to the destination and end the animation loopif((current_time*speed)/(duration*1000)>=1){map.getSource('courier').setData(destination)}else{// find the current distance the courier has traveled along the pathvarcurrent_distance=current_time*speed)/(duration*1000)*distance;varwaypoint=turf.along(path,current_distance,"kilometers");map.getSource('courier').setData(waypoint);// continue the animation looprequestAnimationFrame(animate);}}animate(start);}

To learn more about Mapbox GL JS and how you can implement your projects with it, check out our Mapbox GL JS Fundamentals guide, or take a look at some of the examples.

Your maps are about to get faster thanks to some newly updated vector tiles and a soon-to-be-released version of Mapbox GL JS using Earcut.

Check out this side-by-side video of Mapbox GL JS 0.19-dev and 0.18 on an old laptop.

Stay tuned for an in-depth explanation of Earcut and these improvements.

Last week we launched a redesign of Mapbox Outdoors, our general use map tailored for hiking, biking, and the most adventurous use cases. It now incorporates more lightly styled road and transit details for navigational context, but remains focused on terrain and the most important outdoor features.

Beautiful mountains

From the highest summits to rolling hills, Mapbox Outdoors showcases the elevation of the land. At low and mid zoom levels, detailed hillshading describes the terrain without obscuring other important features, like place labels, administrative borders, and major road systems.

At high zoom levels, high-contrast contour lines and labels give more precise details about an ascent’s slope. Prominent mountain peak labels (with elevation) highlight these significant natural landmarks.

Natural features and green space

Mapbox Outdoors displays large parks, such as national parks, in a traditional tint band style to delineate park borders and more clearly reveal the features within the park.

We’ve designed this map with more differentiation between parks and other green space and landcover, such as golf courses, gardens, zoos, and woods.

Additional information for agricultural landuse adds context to otherwise sparse areas.

We’ve also highlighted other natural landcover and features – such as glaciers, wetlands, cliffs, and rivers – using unique colors and patterns.

Highly visible and distinct paths

Many outdoor activities take place along paths, not high-speed highways. We visually separated paths from other roads and brought them to the foreground, so path networks are obvious at a glance. Since not all paths are equally relevant for all activities, we created distinct styling for walking paths, cycleways, trails, stairs, tracks, and ski paths.

Points of interest for outdoor activities

Mapbox Outdoors features prioritized labels for outdoor amenities, such as public toilets, drinking fountains, and picnic sites. In addition, we’ve filtered out the clutter of smaller, less relevant POIs so you can focus on that bike path or park entrance – not every clothing store and dental office.

Explore Mapbox Outdoors further, and start building with it today!

Today is Bike to Work Day in D.C.! Using Mapbox cycling directions, we made a handy ride planner to plot your route, and highlight any of the 84 pit stops you might pass along the way. To customize your route, click and drag the endpoints below:

To build something similar for your application, check out our developer documentation.

Last week we launched rio-color– an open source Rasterio plugin for color adjustment of geospatial rasters. We’re using it as part of our production image processing chain to keep our satellite basemap looking great and running smoothly.

Rio-color exists for one reason: to correct and adjust colors of geospatial imagery. Satellite, UAV and aerial photography comes to us from dozens of different sources. The color balance differs depending on the weather, the air quality, the seasons, the time of day, the sensor used to take the image, and so on. All this imagery has to be adjusted to be accurate, consistent and visually appealing across the global basemap at all zoom levels.

Rio-color provides three basic color operations: sigmoidal contrast, gamma adjustment and saturation. These can be applied to an input raster dataset in various combinations to produce the desired color balance.

Sigmoidal adjustment can alter the contrast and brightness of an image in a way that matches human’s non-linear visual perception. It works well to increase contrast without blowing out the very dark shadows or already-bright parts of the image.

Gamma adjusts color values according to a power law, effectively brightening or darkening the midtones. It can be very effective in satellite imagery for reducing atmospheric haze in the blue and green bands.

Saturation can be thought of as the “colorfulness” of a pixel. Highly saturated colors are intense and almost cartoon-like, low saturation is more muted, closer to grayscale. You can adjust saturation independently of brightness and hue, but the data must be transformed out of the RGB color space.

These three operations can be composed to do most of the color manipulation necessary to make imagery look better, more realistic and more consistent across our basemap.

Rio-color was designed specifically for large remote sensing jobs where geospatial referencing, memory usage, concurrency, efficiency and scalability are critical. It is one of a growing number of Rasterio plugins, a suite of raster data processing modules using Rasterio as a common framework.

Camilla, Damon, Sean, and Virginia will be in Portland the week of May 30th for the PyCon conference, where they will be working on Rasterio plugins and other parts of the Python mapping stack. Stop by and say hello!

Next week a bunch of us will be in Portland, Oregon for PyCon and we’re excited to meet you there!

PyCon is the biggest annual meeting of the developers and users of the Python programming language. We’re attending to learn about new discoveries, experiences, and opportunities from the Python community and to share our own experience in provisioning servers, refining geocoding data, processing satellite imagery, and applying machine learning to color correction and support ticket prioritization.

One of the things that makes PyCon unique is that it is run by the Python community for the community. We’re helping out by volunteering and by sponsoring the development sprints which begin on Thursday, the day after the main conference activities. Several of us will be staying later in the PyCon week to sprint on Rasterio and other parts of the Python mapping software stack. I hope you’ll consider joining us if you have an interest in making maps with Python.

Find us at PyCon by the Mapbox rainbow stickers on our badges or ping us on Twitter to learn more about Mapbox, mapmaking with Python, and how we work: @bluebweee, @camillacaros, @dnomadb, and @sgillies.