Mapillary is the street-level imagery platform that scales and automates mapping using collaboration, cameras, and computer vision.
A look at pedestrian infrastructure globally
The extent to which pedestrian data has been mapped in OpenStreetMap varies significantly. In this post we'll look at 5 distinct cities and how closely OpenStreetMap matches the reality on the ground.
Preparing for the new Mapillary API
Over the coming months we will be launching version 4 of our API and will be phasing out version 3. This post contains some key information for developers and how they can handle the transition.
Reflecting on 2020
2020 was a year many of us will never forget. Before 2020 gets too far behind in the rear-vision mirror, we'd like to reflect on some of the highlights for Mapillary and our community. We hope you enjoy these reflections.
Upgrading to Vistas 2.0
Posted on 18 Jan 2021
Today we are making available Mapillary Vistas 2.0, a major semantic annotation upgrade for our street-level image dataset of 25,000 images from around the world. We are increasing the label complexity by almost doubling the amount of semantic categories, and additionally provide an approximate depth order of objects shown in the scenes.
Turn a Tesla into a mapping vehicle with Mapillary
Tesla vehicles have often been described as “computers on wheels”. They also include cameras for assisted driving features. In the Mapillary community, these features have raised the question whether the imagery from the car’s cameras could be collected and uploaded to Mapillary. In this post, we present a solution to do exactly that.
Extending Object Detections to Scene Classes
Posted on 16 Sep 2020
Today, we are announcing the extension of machine-generated detections to scene classes. The new scene classes cover transportation infrastructure such as gas stations, toll stations, and parking lots and will help cities and community groups speed up their mapping efforts.
Updating our Street Level Image Privacy Blurring
We recently made changes to how we handle image blurring for street level images. This post provides an update and explains those changes for our Mapillary users and contributors.
Jan Erik Solem
Towards safer transportation: #map2020 in 2020
After almost 3 months of mapping, groups from around the world have submitted their Map2020 reports. One project has been selected to present at the 2020 Humanitarian OpenStreetMap Summit which will be held later this year.
Learning with Verification: Improving Object Recognition with the Community’s Input
Thanks to our community that verified over half a million machine-generated object detections, we’ve developed an efficient approach to object recognition that helps improve map data quality. We used the verifications to include partially annotated images in our training data, leading to much higher detection accuracy compared to using only fully annotated images. This is a scalable way to get diverse training data for developing models that perform well on real-world object recognition tasks globally.
How to Use Mapillary Data in Jupyter Notebooks
Jupyter Notebook is popular among geospatial engineers and it's often used in conjunction with platforms like ArcGIS. This is how you can make the most out of Mapillary data in Jupyter Notebook.
We're back with the first CompleteTheMap of 2020! Here's how you can take part.
How to use Mapillary point data in OpenStreetMap iD
Mapillary has long been integrated into OpenStreetMap editors, and we are evaluating how to increase the value of both the imagery contributed by our community as well as the data extracted from that imagery. As it stands, Mapillary’s world-renowned computer vision capabilities provide an excellent tool for fixing maps in an unusual way. Outside of Mapillary imagery, we are seeing machine learning applied toward satellite imagery and telemetry data to help map roads and buildings. The coupling of computer vision and street-level imagery, however, offers a better view of the detailed reality maps have often lacked.
Street-Level Images for Better Public Transportation in Latin America
In Latin America’s rapidly growing cities, policymakers have struggled to keep up with changes in public transportation—especially around private services that have developed spontaneously for decades. With tools like Mapillary, communities have the ability to help collect the data that highlights their own experiences and needs, allowing their governments to develop regulations that can ultimately make for safer and more environmentally friendly transportation systems.
Innovative Uses for Mapillary in Field-Based Research Projects
Street-level imagery from the Mapillary platform is not only helping to build better maps all over the globe, it has also found its way into new and exciting research projects. We recently spoke with three researchers who have found innovative uses for the Mapillary platform in salmon habitat restoration, documenting the experience of a Victorian cemetery, and the fight against invasive pests.
New Optical Flow Records using Mapillary’s Five Elements of Flow
In our latest work we reveal five key techniques for improving optical flow prediction — the task of estimating apparent 2D motion of every pixel in two consecutive images from a video. Our findings are the result of carefully analyzing shortcomings in existing works and thus help to improve a wide range of them. We quantitatively and qualitatively surpass the performance of directly comparable works and set new records on challenging optical flow benchmarks.
Achieving New State-of-the-Art in Monocular 3D Object Detection Using Virtual Cameras
We are introducing a new way of doing 3D object detection from single 2D images. The architecture is called MoVi-3D and is a new, single-stage architecture for 3D object detection. Starting from a single 2D image, it uses geometrical information to create a set of virtual views of the scene where the detection is performed using a lightweight infrastructure.
A Look Back at 2019 and the Road to One Billion Images
The Mapillary platform now hosts one billion high-resolution images from around the world, all open and available for improving maps. With 2019 drawing to a close, we are looking back on some of the events that helped us reach this important milestone.
Jan Erik Solem
Unveiling our Latest Research: Multi-Object Tracking and Segmentation from Automatic Annotations
Access to high-quality training data is one of the most important requirements to push the boundaries with machine learning in computer vision. Today we’re unveiling our latest piece of research, where we roll out an entirely new way to generate training data for multi-object tracking and segmentation. The approach turns raw street-level videos into training data with unprecedented quality—even compared to results based on human-annotated data. By allowing machines to generate training data, the cost for training computer vision models can go down substantially. We validate our approach for multi-object tracking and segmentation and obtain new state-of-the-art results. Here is how.
The Data That Paves the Way: How We’re Building the First Open Dataset for HD Maps
Posted on 20 Nov 2019
We are building the first open dataset for maintaining and updating HD maps with Zenuity, AstaZero, RISE, and AI Innovation of Sweden. Together, we will collect map data in a highly controlled environment through cheap dashcams, lidar, and radar, in an effort to build a cost-effective way of updating HD maps and teaching autonomous vehicles to understand their surroundings through an HD map — even when the surroundings have changed remarkably.
Helping Cities Across the US to Understand Their Streets: Unveiling our Partnership with IWorQ
We’re celebrating GIS Day by unveiling a new partnership with IWorQ, the cloud-based software provider that helps 1300 US municipalities to manage their assets, facilities, licenses, and more. Together, we will help local governments all over the US to capture street-level imagery and understand their streets and roads through computer vision. With the help of computer vision technology, municipalities will get access to more and better data, which will improve decision-making and cut costs.
Accessing Machine-Extracted Map Data: Improved Data Downloads
Downloading the map data that you’ve subscribed to is now much more flexible. You can choose the object types you wish to download, which also means your download files will get prepared faster. There are two new file types available, and notifications for when your data is ready.
Mapillary, OpenStreetMap, and OSGeo: State of the Map and FOSS4G Retrospective
August and September proved to be a busy and exciting time of year for Mapillary, with travel and presentations to some of the most important conferences in the open geospatial world. Our team was present in Bucharest, Minneapolis, and Heidelberg to talk about Mapillary’s role in fixing maps and growing the largest collect of global street-level imagery. Read on to hear about what we presented at State of the Map, State of the Map US, and FOSS4G!
The Camera That Turns Any Fleet into a Mapping Fleet: Say Hello to the Mapillary Dashcam
Mapillary is releasing the world’s first end-to-end encrypted mapping dashcam to help delivery and ride-sharing fleets access the map and navigation data they need to serve their customers. The camera is light, flexible, and while it requires no extra work from drivers, it can capture up to 150,000 high-quality images in one eight-hour session. The images and the data are available on the Mapillary platform within hours of uploading. Finally, a quick and easy way for fleets to get the map data they need to fix maps.
Towards a driverless future: How Mapillary is teaming up with Siemens to teach streetcars to see in a fully autonomous depot
Posted on 08 Oct 2019
Mapillary is teaming up with Siemens, Germany’s Federal Ministry of Transport and Digital Infrastructure, and others to make driverless streetcars in a self-operating depot a reality. The project takes place in Potsdam and will over the course of three years teach a driverless streetcar to get from A to B with the help of sensor fusion and street-level imagery that Mapillary is turning into map data to allow the streetcar to see.
Announcing the Second Global Verification Challenge
Verification projects help train the algorithms that identify objects in street-level imagery. More verifications mean more accurate detections, and that means better maps for everyone. Join us as we strive to complete one million verifications and compete for cameras and other prizes.
More Ways to Teach the Machine to See: Spotting Missed Objects
With Verification Projects, you can do quality review of the automatic object detections that underlie Mapillary’s machine-generated map data. In addition to verifying whether detections are correct, you can now also check images for missed objects, as we release a new type of task that’s now available in all verification projects. This will help improve our algorithms even more, enabling you to scale up mapping with high-quality map data.
Protecting Privacy in the World of Better Maps: How Collaboration Paves the Way
Roughly 2 million images are uploaded to Mapillary every day. Mapillary’s computer vision algorithms automatically anonymize all images by blurring sensitive information like faces and license plates. Today we’re happy to reveal that our blurring algorithms are the best available for anonymizing street-level imagery. By uploading imagery to Mapillary, you get all the data you need without compromising on privacy.
Mapping Accessibility in Istanbul and Beyond
Mapillary’s Community Operations Manager, Said Turksever, introduces us to his passion project—mapping accessibility issues and wheelchair access. Through ICT4Society, Said introduces young people from across Europe to open data and mapping tools. In the process, they build accessibility maps to help government officials make better decisions for their citizens.
Welcoming lvl5 Imagery and Contributors to Mapillary
Today we’re announcing hundreds of millions of new images being added to Mapillary. Following lvl5 moving on to new adventures, all images they collected through paid drivers are now coming to Mapillary—and made available to everyone.
Jan Erik Solem
Mapping the Galápagos Islands for a Better Understanding of Human Impacts on the Environment
Last summer, a team of researchers from the University of Chicago embarked on an ongoing project to map the Galápagos Islands using Mapillary. Annually mapping the islands with street-level imagery contributes to a better understanding of how people affect the natural environment and our very own Chris Beddow had the opportunity to assist the team this summer with their important work.
Looking Back at Esri UC 2019
Every July, Esri hosts its annual User Conference in sunny San Diego, California. Rumors say that attendance was at a record level this year, and it certainly set records for Mapillary. Six members of our team gathered from five different cities around the world to represent Mapillary. Aside from the world-class team at our exhibition booth, our latest demos and case studies helped make for a very busy week talking to new and potential customers.
Announcing the Winners of #map2020
There were 33 projects submitted for the #map2020 challenge to build better maps in undermapped regions. Mapping teams joined in from Europe, Asia, and Africa for the chance to demonstrate how street-level imagery can play a role in addressing a humanitarian challenge. Two projects were selected to present at the HOT Summit in Germany.
How Innovative Student Research Projects Help Map the World
Mapillary is a great tool for student mapping projects since the platform allows anyone, anywhere, to map what is important to them. Whether traveling over rugged mountain terrain or through the jungle, anywhere you can bring a camera, you can map with Mapillary.
Introducing the Mapillary Partner Program
Many businesses and organizations, ranging from governmental to geospatial and mapping, already take advantage of our global street-level imagery platform to provide solutions for their customers. Today, we are making it easier than ever by announcing the latest and greatest iteration of our partnership program.
Introducing the Mapillary Traffic Sign Dataset for Teaching Machines to Understand Traffic Signs Globally
Today we’re releasing the Mapillary Traffic Sign Dataset, the world’s most diverse publicly available dataset of traffic sign annotations on street-level imagery that will help improve traffic safety and navigation everywhere. Covering different regions, weather and light conditions, camera sensors, and viewpoints, it enables developing high-performing traffic sign recognition models in both academic and commercial research.
The End of the Map as We Know It
Whether it’s OpenStreetMap, HD maps, our mobile navigation and map applications, the up and coming story is often the same theme: everything is about to change. Mailing list discussions and State of the Map presentations have both mourned and praised OpenStreetMap in this light. The arrival of cutting edge methods of collecting or extracting map data has left many wondering if there is space for a community and the touch of human hands.
From Ghana to Iraq: How #Map2020 Mappers Use Street-Level Imagery to Tackle Humanitarian Challenges
From Ghana to Iraq, Zambia to the Philippines, we take a look at some of the 33 mapping projects that are taking part in #map2020 to build better maps in undermapped regions. The mapping participants are collecting street-level imagery to improve things like waste management, natural disaster response, and damaged roads, addressing some of the most pressing issues in undermapped regions.
Visualizing the 3D World: A Practical Approach to Polygon Triangulation on the Sphere
Mapillary uses semantic segmentation to understand the contents of each image on the platform. We visualize the segments with different colors overlaid on the image objects. This is quite the challenge when done on 360° images, which are rendered as spheres in the Mapillary viewer. Let's take a look at our approach to solve the spherical visualization problem.
Training Machines to Attain a 3D Understanding of Objects from Single, 2D Images
We sit down with Peter Kontschieder, the Director of Research at Mapillary, to talk about “Disentangling Monocular 3D Object Detection”, the latest academic paper to be published by Mapillary’s Research team. Peter tells us about how 3D object detections made in single 2D images have the ability to improve mapmaking and push down the cost of autonomous vehicles, and how the team unveiled a fundamental flaw in the metric used by the most dominant benchmarking dataset in this area.
Effortless Mapping with a Customized Mapillary Dashcam
Dashcams are the perfect type of camera for hours of imagery capture without needing much interaction from the driver. That’s why we developed the Mapillary edition of BlackVue DR900s, a high-resolution and high-performance dashcam, explicitly optimized for effortless mapping. This custom dashcam makes image collection more efficient while simplifying uploads, enabling both individual mappers and entire fleets to scale up fresh imagery collection for better maps everywhere.
Against All Odds—Mapping by Bicycle Along the Elbe in Spring
Mapillary is a distributed team of 55 employees in eight different time zones and twice a year we all get together at company offsites. We are all mappers at heart, so after the last gathering Tobias Ollmann, one of Mapillary’s Computer Vision Engineers, decided his trip home to Graz, Austria would be the perfect time to capture imagery for the Mapillary platform. For this reason, he decided to ride his bicycle 1,500 km from Sweden to Austria, and even though all of the odds seemed stacked against him, he was determined to finish his ride.
Geochicas: Helping Women Find their Place on the Map
In 2016, Geochicas was created by a small group of women in OpenStreetMap who noticed a structural issue in data communities due to the lack of female participation and project leadership. By promoting work that analyzes how women are represented in geospatial and technological spaces, they are helping to improve the overall diversity and quality of the data that goes into OpenStreetMap.
How to Use Mapillary Data in OpenStreetMap
Mapillary has been processing street-level imagery with computer vision in order to extract traffic signs for several years, but in 2019 we’ve started to offer an expanded dataset. Point features such as fire hydrants, crosswalks, and manholes are also now available, with their location approximated on the map thanks to 3D reconstruction of scenes combined with segmentation of the images where these objects are recognized. To date, Mapillary has identified more than 191 million of these map features, and one of the results is GeoJSON data that can be used to enhance a map.
Map Data on Demand: Announcing the Mapillary Marketplace
Today, we launch the world’s first demand-driven marketplace for generating street-level imagery and map data. Mapmakers, cities, and NGOs can post mapping requests that anyone can pick up and help complete. Mapillary’s collaborative contributor network already spans 190 countries and is open for everyone to join and work on map data projects to improve maps, cities, and mobility everywhere.
Jan Erik Solem
Scale Imagery Collection with Capture Projects: Android App Now Available
With our recently released Capture Projects, organizations like the City of Detroit have collected thousands of kilometers of fresh street-level imagery by having a team of drivers cover the area, task by task. Today we’re making Mapillary Driver, the app that drivers use for completing their tasks, available on Android. This improves access to Capture Projects as teams are now able to use a broader range of devices in the field to see what they need to map.
Announcing #CompleteTheMap V
#CompleteTheMap is back for the Northern Hemisphere summer. We’re encouraging location submissions from around the world with the goal to capture dense street-level imagery within the challenge area. The top three participants will be awarded cameras for the contributions at the end of the challenge.
Launching #Map2020: A New Campaign for Building Better Maps in Undermapped Regions
Mapillary and Humanitarian OpenStreetMap Team are joining forces to accelerate map data collection in undermapped regions. Local mappers are invited to use street-level imagery in mapping projects that address humanitarian or developmental challenges. Two of the projects submitted to the #map2020 campaign will be selected for a fully-funded trip to HOT Summit in Heidelberg, Germany this September.
Building the Global Street-Level Imagery Platform: How We Test Software at Mapillary
When building a global platform for street-level imagery, there are lots of moving parts in the system that need to work together. At Mapillary, we pay a lot of attention to quality assurance and have built a testing framework that is reliable, developer-friendly, and scales as we keep developing the platform further. Here is how we do it.
Five US Departments of Transportation Upload 270,000 Miles of Road Data to Mapillary to Understand Road Safety
Road safety is a big problem in the US and a key focus for states across the country. In a big step towards ensuring road safety for their residents, Departments of Transportation in Utah, Florida, Arizona, Connecticut, and Vermont have now all made their state photologs publically available on the Mapillary platform. These images cover 270,000 miles of roads and are automatically processed with computer vision to detect guardrails, traffic signs, and pedestrians’ interactions with vehicles at scale.
Improved Viewing Experience: Announcing Combined Panning in the Mapillary Viewer
We've just enhanced the Mapillary viewer with the Combined Panning feature that lets you pan between overlapping regular images just like you would do with a full panorama, simply by dragging with your mouse. This provides a more natural and convenient viewing experience as you navigate among Mapillary imagery to look around in an area.
500 million images available to everyone
We just hit another milestone for our platform. More than 500 million images have been uploaded to Mapillary by our amazing community, customers, and partners worldwide.
Jan Erik Solem
Announcing Verification Projects: A Scalable Way of Validating Machine-Generated Data
With Verification Projects, map makers and GIS experts can review machine-extracted map data by adding human validation to the workflow. Comprising a project dashboard and a simple game-based tool for verifying object detections, Verification Projects enable quality assurance of the data available in any area, as well as help to improve Mapillary’s machine learning algorithms with human feedback.
Improved Upload Management in the Desktop Uploader
Uploading thousands of images got even easier with the latest release of the Mapillary Desktop Uploader, which adds functionality for previewing images and managing your upload history. Spend less time handling your uploads and more time improving maps!
Announcing Capture Projects: An Entirely New Way of Collecting Map Data at Scale
Today we’re announcing Capture Projects to help cities, mapmakers, and transportation agencies everywhere ramp up imagery and map data collection. With the release of a new project dashboard and an accompanying app, called Mapillary Driver, organizations can now manage an unlimited number of drivers to capture street-level imagery at any given point, rapidly escalating how quickly maps can be updated at scale.
From Sideline to Center: The Potential for OpenStreetMap to Seize the Curb
As technology evolves, there is a growing need for more detailed information in maps. Curbs are a great example of a street asset that has a wide variety of purposes, which puts increased pressure on maps and the details they do—or rather, don’t—provide. In this post, Daniela Waltersdorfer J joins Mapillary's Christopher Beddow to explore the potential for OpenStreetMap to embrace the curb.
Doing way more with Less: Catching up with the Mapillary Research Team
Since 2016 when we opened our AI lab in Graz, the research team has been busy publishing papers, winning benchmarking competitions, and developing the building blocks that power Mapillary. Now we are celebrating the opening of a brand new Graz lab and looking back at how it all came together.
Updating Maps with Cameras: 42 New Object Classes Now Available as Map Features
Today we’re announcing 42 new types of map features that Mapillary extracts from street-level imagery and automatically positions onto the map. Ranging from utility poles and streetlights to mailboxes and manholes, this will help cities, mapping companies, and transportation agencies keep their maps up to date using cameras.
2018 Year in Review
2018 is coming to a close, and it’s that time of year where we look back at some of the best moments.
Jan Erik Solem
Mapillary in Japan
The Mapillary community is spread across the world, with significant contributions coming in from Finland in the north, Argentina in the south, and—if we’re slicing the world in half by the international dateline—the USA in the west and Japan to the east.
Exploring Zanzibar with Mapillary: Pt 2 - The End of the Road
After the labyrinth of Stone Town’s alleys and a ship wreck on the west coast, our story heads east. This blog post is a continuation of Pt 1 - The Chief Officers’ Logbook. If you haven’t read part 1 of our adventures in Zanzibar, start there. If you have, welcome to part 2!
Automating the Process: Collecting Traffic Sign Data in Clovis
In many one-person GIS shops across the United States, collecting and managing local assets can be a daunting task. For Steven Hewett of Clovis, New Mexico, Mapillary presented a way to streamline and automate the process of completing a city-wide traffic sign inventory.
Launching the Better, Faster, Upgraded Mapillary Web
We have just launched an upgraded version of our website that features notable improvements (up to 80%) in page loading times. In addition, we’ve implemented the latest MapillaryJS release which helps straighten up distorted images and make transitions smoother between images. With this, we’re offering a much faster and better experience when using the platform.
Improving the Viewing Experience: Image Undistortion in MapillaryJS
Radial distortion is a common problem when the 3D world is represented in 2D images. With the latest MapillaryJS release, we are now undistorting every image. This leads to a better 3D representation and improves the viewing experience of Mapillary images.
Map the Alps with Mapillary, HERE, and Garmin
Map coverage in the Alps needs improving. That’s why Mapillary, HERE, and Garmin are offering prizes for mappers who increase map coverage in the given areas. The Alps are visited by over 120 million people annually, so up-to-date maps are important. Here’s how you can help (and how you win one of the prizes!).
Balancing Urban Infrastructure: Perspectives from Valencian Cyclists
Citizens and the city council in Valencia, Spain, are working together to increase the safety, efficiency, and popularity of cycling. They use street-level imagery to provide a visual reference of the current situation and extract data for updating maps. This improves information exchange between stakeholders and helps the city government decide how to allocate the €4.5M budget assigned to developing bicycle infrastructure.
More Flexibility to Compare Images with MapillaryJS
After the recent update of MapillaryJS, you can now compare any pair of images, regular as well as panoramas. It also features a simpler design to make MapillaryJS integrate smoothly in various applications.
Oscar Lorentzon, József Schaffer
Creative Commons Summit and How Mapillary Makes Global Relations
Peter Leth, our active contributor and educational advisor for Creative Commons Denmark, represented Mapillary at the Creative Commons Global Summit. He shares his insights (and an unexpected meeting) in this blog post—published under the CC BY license, giving everyone the ability to share, copy, distribute and reuse it.
Global #CompletetheMap Returns
The second Global CompletetheMap Challenge will launch on 1 May. Nominate your location and compete with Mapillary contributors across the world—and win awesome prizes!
Accurate Privacy Blurring at Scale
We have developed an accurate and fast algorithm for privacy blurring on street-level images. The new model, based on semantic segmentation, detects 99.9% of identifiable faces and license plates and is optimized for large-scale processing.
Scaling up with a $15M Investment
BMW i Ventures, Samsung Catalyst Fund, and NavInfo join our existing investors to support growing the world’s largest collaborative street-level imagery platform for extracting map data using computer vision.
Jan Erik Solem
March 2018 Leaderboards
To keep the competitive spirits up, we're publishing a series of monthly leaderboards. Take a look at who contributed with most images, edits, blurs, and verifications in March.
Mapping and Machines: Level up with AI
Knowledge and supervision from humans, speed and scale from machines. This is how we can keep up with the freshness, coverage, accuracy, and high level of detail that we require from our maps.
Jan Erik Solem
Let's Visualize the World with AI and GIS
GIS specialist Sanna Jokela from Mapillary's partner Gispo was invited to give a speech on AI and GIS. This is what she found out, exploring the unfamiliar topic of artificial intelligence.
February 2018 Leaderboards
To keep the competitive spirits up, we're publishing a series of monthly leaderboards. Take a look at who contributed with most images, edits, blurs, and verifications in February.
MapillaryJS Now Enables Fast Sequence Navigation
With the recent update of MapillaryJS, we have added controls for fast sequence navigation and better sequence overview. Now you can easily navigate to the beginning, end, or any other position of interest in a sequence.
Challenging the Winter Dip, from Australia to Uganda
The first edition of the Global Complete the Map challenge has drawn to an end with remarkable results despite the hardships of the winter in the Northern Hemisphere. Here’s a recap of what was achieved and what’s planned next.
What We Learnt about GIS at the Mapillary Meetup
Conversations at the first Mapillary GIS Meetup had a recurring theme. GIS work can include some cumbersome processes, but the experts working in the field have lots of creative solutions. Mapillary’s role is to provide the tools that help bring these to life.
January 2018 Leaderboards
To keep the competitive spirits up, we're publishing a series of monthly leaderboards. Take a look at who contributed with most images, edits, blurs, and verifications in January.
Mapillary for ArcGIS Pro Enters Public Beta
Mapillary for ArcGIS Pro is now available as a Public Beta with considerable performance improvements compared to the previous version. We invite you to join directly from the Mapillary for ArcGIS page.
Tracking Placemaking in Public Spaces
In this guest post, Anna Siprikova from Project for Public Spaces explains how they used Mapillary to make the process of analyzing public spaces easier, faster, and available to everyone.
Mapillary GIS Meetup—Malmö, 6 Feb
Join us for an evening of GIS talk and demos to learn how street-level imagery can help automatize road and street inventories.
Between the Lines in Helsingborg: Locating Assets via Imagery
Helsingborg, a forward-thinking Swedish city, makes visual geospatial data available on Mapillary for both staff and the public. Automatically extracted map features complement the city's geospatial records and help get an overview of Helsingborg's assets.