OpenStreetCam plug-in – Display OSMElement

A new feature has been added the OpenStreetCam plug-in that allows the visualization of the OSM Elements corresponding to the selected detection or cluster. OSM Elements are the basic components of OpenStreetMap’s conceptual data model of the physical world.

How to use the new feature

The first step is to select a detection or a cluster. If the selected item has OSM Elements, the Matched Data button from the details panel will be enabled.

Detection selected
Aggregated detection selected

By pressing the button, the corresponding OSM element will be drawn in the OpenStreetCam layer and it will persist until selection changes or the item is deselected.

There are three possible cases of display:

  • The element is of type Node:
Nodes are displayed using the JOSM node icon.
  • The element is of type Way:
Ways are displayed as a red line.
  • The element is of type Relation:
“From” element is displayed as a green line
“Via” element is displayed as a blue line
“To” element is displayed as a red line

Tags

Relations have a checkable option under preferences for tag display.

When this checkbox is selected, the tags associated with the OSM element members are displayed on the map.

Relation displayed with tags.
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Announcing the Second OpenStreetCam Australia and New Zealand Competition

The Telenav OSM team is excited to announce a new OpenStreetCam imagery collection competition for Australia and New Zealand! This second edition will kick-off on the 15th of March and will last until the 15th of April. Buckle up!

How to compete

  • Download the free / open source OpenStreetCam app for Android or iOS.
  • Collect as many OpenStreetCam points as you can until April 15, 23:59 UTC+10. Every image uploaded gets you points, with more points awarded for roads that are being covered for the first time or that have been surveyed infrequently.
  • We will announce the winners on the 16th of April.
  • The top contributors will win a Waylens Horizon dashcam.
  • We will give out one dashcam per country to our top contributors.
  • The runners up will receive a $100 Amazon gift card.
  • You need to gather at least 250K points to be eligible to win the dash cam.
  • Points collected with Waylens dashcams are not eligible.
  • It costs nothing to compete.

About OpenStreetCam

For those who don’t know OpenStreetCam, it is a free and open platform that uploads street-level imagery captured by your smartphone and detects salient features from the uploaded images such as signs, lanes and road curvature to improve OpenStreetMap. The images are collected by mappers for mappers and can be used in iD or JOSM to improve OpenStreetMap.

We have a ‘Getting Started’ guide on our blog!

FAQ

  • How will I know my rank in the leaderboards?

We will post weekly updates on Facebook and Twitter. Make sure you check them once in while.

  • How can I earn more points?

Go to https://openstreetcam.org and check out where the magenta lines are missing. Those areas will guarantee the highest return. 

  • I’m getting errors trying to install the app, record or upload.

Send us an e-mail at hello@openstreetcam.org and we’ll provide support and troubleshooting ASAP.

  • Does your app provide routing?

No.

  • Can I use an OBD-II dongle?

You sure can, if you have one to spare. It’s going to enable the 2X multiplier per each kilometer collected.

  • Can I record with a dashcam?

Of course, you have our full support, as long as the data is under the form of geotagged JPEGs. You can upload the data collected via our script.

  • I signed up via OSM, how will you contact me?

Please send us a message on Facebook or drop an e-mail at hello@openstreetcam.org.

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After Competition, OpenStreetCam Can Now Detect Dozens Of Sign Types in Australia and New Zealand

Last December and January, OpenStreetCam held a image collection competition in Australia and in New Zealand. The three mappers in each country who collected the most points during the months of December 2018 and January 2019 could each win a gift card: $100 for the winner, and $25 for the second and third place. We just announced the winners to the communities in both countries. Congratulations to steve91, robbie-bloggs, ConsEbt, david-blyth, ivss-xx, and nicknz!

User-uploaded image: Screenshot 2019-02-06 15.50.05.png
OpenStreetCam coverage in Melbourne after the competition

We decided to do these competitions because we wanted more mappers in Australia and New Zealand to get acquainted with OpenStreetCam, and consider contributing to this free and open platform for street-level images. The more contributions, the more help OpenStreetCam can be for OSM mappers! There weren’t many contributions in either country yet, and if you go to the OpenStreetCam web site, you’ll quickly see that there are still large gaps to fill. Still, OpenStreetCam coverage grew by 800% since the beginning of December.

Head over to the ImproveOSM Blog for a step-by-step guide on how to get started with OpenStreetCam yourself!

User-uploaded image: Screenshot 2019-02-06 15.52.07.png
OpenStreetCam now sports almost 1.5 million images in Australia and New Zealand combined

OSM mappers can use the OpenStreetCam images to help with mapping. You can’t see everything from an aerial image. Signs are a great example of useful mapping information that requires an on the ground perspective. This is where OpenStreetCam is particularly handy, because we detect an increasing diversity of signs that appear on the photos for you automatically, using an open source machine learning platform.

Interested in a more in-depth look at the OpenStreetCam sign recognition AI? Have a look at our talk at State of the Map 2018 in Milan or our talk at State of the Map US last fall!

Using detected signs in JOSM

Results

For the sign detection platform to work and detect a variety of signs reliably, it needs training data. Your contributions during this competition have been invaluable to reach that goal. Our Map Team looked at tens of thousands of images collected by the community during the competition in Australia and New Zealand, and validated more than 160,000 traffic signs found in these images. After feeding that data into the platform, we can now reliably detect more than 80 types of signs in Australia and New Zealand. As we continue to look at more images that you contribute, the system will get smarter and we will detect more different types of signs.

Do you want to help train the OpenStreetCam sign recognition AI? You can do this right from the OpenStreetCam web site. Read all about it in this blog post.

Validating automatic sign detections is a quick an easy way in which anyone can help improve OpenStreetCam’s ability to detect signs reliably
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OpenStreetCam JOSM plugin – new features

The Telenav OSM team just released a new version of the OpenStreetCam JOSM plugin. In the last couple of months we had improved our sign detections and improved the detection map view,  by displaying aggregated traffic sign detections instead of individual detections. Traffic signs are detected per OpenStreetCam photos, in dense areas the same traffic sign is detected several times. Checking several detected signs that represent the same traffic sign in real life is time-consuming and slows down the mapping process. In order to improve the mapping process, detections that represent the same traffic sign were aggregated into a single detection.

Map View Changes

Aggregated detections are displayed for high zoom levels starting with zoom level 16. Each aggregated detection is represented by a traffic sign cluster icon rotated based on the detection heading.

Aggregated detection data

An aggregated detection can be selected by a right click from the map and unselected by a double click.                                                                                        When an aggregated detection is selected the detection icon along with the belonging photo locations are highlighted on the map and its information is displayed in the right side OpenStreetCam Detection panel. The photo on which the detection has the best visibility is also loaded automatically.

Loading other photos that contains the detections can be done by either selecting a photo location from the map or by pressing the Next/Previous buttons from the OpenStreetCam Detection panel. For the associated shortcuts take a look at the OpenStreetCam shortcuts from JOSM Preferences.

By default, the plugin displays the photo locations of a selected cluster, but the plugin can be configured to display also the actual detections. This can be enabled from JOSM Preferences->OpenStreetCam plugin -> Aggregated detection settings.

If enabled individual detections are connected to the corresponding photo. This visualization is used mainly for debugging purposes.

Data filters

The OSC plugin data can be filtered based on various new filters. Some of these filters were already present and others were extended.

  • data to display – the type of data to be displayed for high zoom levels; by default, we display OpenStreetCam photos and aggregated detections; if both detections and aggregated detections are selected then besides aggregated detections only the detections that do not belong to any aggregation are displayed
  • only mine – displays OpenStreetCam data contributed by the logged in user
  • not older than – filters out data based on a given timestamp
  • detection filters – are applied to detection/aggregated detection data
    • mode – is applicable only to individual detections; if set filters data based on detection mode; manual detections are detections that were manually marked on the OpenStreetCam photos, while automatic detections are detections recognized automatically by our platform
    • edit status  – is applicable only to individual detections; if set filters detections based on edit status (if the user had edited or not in OSM the detection)
    • OSM comparison – filters the data based on the status;  OSM comparison represents a status of a detection regarding its presence in OpenStreetMap; this filter is useful since the mapper can visualize only detections that need to added to the map
    • detection type – filter data based on detection type and subtype; this filter is useful if the mapper would like to focus on mapping only a certain type of signs

Default filter settings can be reset by pressing the “Reset” button.

If you are interested in the components used for aggregating the traffic sign detections check out the following presentation:  https://youtu.be/3I8lsIttqVA?t=290.

Upcoming features
The JOSM plugin is work on progress, we are working on improving the usability and plan to add new features from time to time. In the near feature, we plan to improve the detection mapping workflows.

We hope that you enjoy the new features! If you have ideas, suggestions or encounter any issue with the plugin during editing sessions please submit either to the GitHub issue page or to the Feedback forum .

Have fun improving the map by using OpenStreetCam images!


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Getting Started with OpenStreetCam

In this post, we would like to guide you in making your very first contribution to OpenStreetCam.

In this post, we would like to guide you in making your very first contribution to OpenStreetCam. There are already 190 million images on OpenStreetCam covering more than 5 million kilometers of road, so obviously getting started is easy enough that it can be done without much guidance 😁 But in case you do need a little encouragement to record your first trip, or just want to see how it works before you try it yourself, read on.

The first thing you want to do is download the free app. OpenStreetCam apps exist for Android and iOS.

The OpenStreetCam app in the iPhone app store

When you first run the app, it will give a quick introduction about OpenStreetCam. Flick through that to get to the main screen. Then go to your Profile, where you can log in.

You can create an OpenStreetCam account by logging in with your existing OpenStreetMap, Facebook or Google accounts. You won’t need to create a separate password.

After logging in through the platform of your choice, you will see your new OpenStreetCam profile 🙂

Your profile will look a little empty compared to mine, but we are here to change that! Let’s go out and drive some.

You will need some sort of phone mount in your car so you can point the camera straight ahead with a clear and unobstructed view of the road ahead. I use an iOttie brand mount (an older version of this one) but any mount that will hold your phone in landscape mode reliably will do.

You will also want to connect your phone to power. We have spent a lot of time optimizing the app, but the recording still drains the battery quite fast.

Okay, we’re almost ready to go. We just need to start recording mode so the app will start taking pictures as you start moving. Before you do that though, take a moment to scroll around the map looking for streets that have no purple lines. That means that nobody has captured any images there yet, so those streets are extra valuable. (You get 10x points for them too.)

When you’re done with that, press the blue camera button to start recording mode.

You may notice that the app mentions a thing called ‘OBD’. This refers to a port in your car that transmits data about the current state of the vehicle. Using a compatible OBD dongle, OpenStreetCam can use this for improved location accuracy. This is optional but gives you twice the points if detected! If you want to learn more, drop us a line.

The app will not immediately start taking pictures. Using your phone’s built in sensors, it will detect when you start and stop moving. As long as you’re stopped, no pictures are taken. This saves space, time spent uploading, and mappers wading through duplicate images of the same location.

In recording mode, you can switch between a big camera view and a small minimap, or the other way around. You switch by tapping on the minimap / mini camera in the left bottom.

As you drive, you will see your points increase as well as some other basic trip stats like number of pictures taken, space used, kilometers driven and recording time. As I mentioned before, roads that nobody has captured before are worth 10x the points. As you collect more points, you get higher up in the leaderboards and level up!

When you’re done driving, hit the record button to end the trip. You will now see a summary screen for your trip, showing where, how long and how far you’ve driven, as well as how many points you have collected on this trip.

Now that you’re done collecting your first images, it’s time to upload them to OpenStreetCam. This does not happen automatically by default (but you can go into settings to change that.) So as soon as you’re connected to wi-fi, go back to the app and go to ‘Upload’. There you will see the trip you just created.

You can tap on the trip to get more details. One cool thing you can do is ‘scrub’ through the trip.

Tap ‘Upload all’ in the top right corner to upload. You will notice that the file size is actually relatively small. That is because internally, the app compresses the images into a video stream that is unpacked into separate photos again at the server side, saving you time and upload bandwidth.

Once the upload is finished, you can go to openstreetcam.org and log in there. Use the same login method you used in the app, so if you used OpenStreetMap to log in on the app, use OpenStreetMap as your login provider on the web site as well.

Once you’re logged in, you can go to your profile to see your trip.

You can click on the trip to see the uploaded images. (Here is the trip I recorded for this demonstration.)

Notice that I should have wiped the snow ❄ off my car before I started recording.. 😬

Finally, I highlighted two icons you see on the lft hand side of your trip detail window. If you are in the U.S., you may see a number badges. They indicate how many street signs were recognized (the bottom one) and how many of those represent data that doesn’t seem to exist in OSM yet. That’s for a future post though!

Signs detected from my trip images. More on that in a future post!


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Help Train OpenStreetCam’s Open Sign Detection Platform

Telenav open-sourced the machine learning based sign detection platform that powers the automatic detection of nearly 100 sign types in the  OpenStreetCam images you contributed. You can already see these detections in the latest version of the OpenStreetCam JOSM plugin to help you map, and iD integration will come soon as well. 

Machine learning gets better with training. The more known instances of a particular sign that are fed into the system, the more reliable the automatic detections for that sign type will become.

Our Map Team has spent thousands of hours manually tagging and validating traffic signs in images, and the resulting training data is open source as well. But did you know you can help improve the detection system yourself as well? Let us show you how.

If you go to the trip details on the OpenStreetCam web site, you will see three ‘tabs’ on the left. The first one takes you to the main trip info. The second one takes you to an OSM edit mode, that lets you quickly go over detections and see if they need to be added to OSM. (Separate post! The third tab is the sign validation mode. If the tab icon has a number with it, there are unverified signs to work on.

The detection validation mode on the OpenStreetCam web site

The bottom part of the screen shows all detected signs. The ones that have been validated already will have a green checkmark with them. The ones that have been invalidated will have a red ‘X’. 

You can validate or invalidate the automatic detection if the sign on the image exactly matches / doesn’t match the automatic detection, by clicking the corresponding button on the left. 

Power Validator Workflow

You can validate entire trips with many detected signs very quickly by using some of the power functions available:

  • Next to the trip slider, underneath the image, you will find a small magnifying glass button. Clicking this will automatically zoom and pan the image to the detection
  • Use Cmd (Mac) / Alt (Windows / Linux) and the left and right arrows to quickly jump to the next detection
  • Use Cmd / Alt up and down to validate or invalidate the currently highlighted detection.
Skipping through detections quickly using shortcut keys Cmd / Alt up and down

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Summer Dispatch From The Telenav Map Team

It has been an exciting summer! Besides our regular work, there was the annual State of the Map conference that we were all really looking forward to. We launched a new ImproveOSM web site. OpenStreetCam dash-cams are distributed to OSM US members. And more. Read all about it in our Summer Dispatch below!

State of the Map

Quite a few of us got to go to State of the Map in Milan, Italy! Our team hosted four presentations at the conference, and we are really happy with the interest and feedback we received. We made a lot of new map friends as well!

All SOTM presentations were recorded and posted on YouTube, so if you missed any of us, you can watch the presentations at your leisure:

Alina and Bogdan presenting our Machine Learning stack at SOTM 2018

We also had a booth at the conference where we talked about ImproveOSM and OpenStreetCam, and where 6 lucky winners received a Waylens OpenStreetCam dashboard camera!

Excited crowd right before one of the Waylens cameras is being given away!

Mapping

We continue to map in Canada, the United States, and Mexico. As always you can track our work on GitHub. We have been focusing a lot on adding missing road names for the larger metropolitan areas in the US. Our typical workflow is to identify local government road centerline data sources, verify the license, process them with Cygnus to find changed / new names, and manually add the names if we can verify them.

Local road centerline data the team identified in Colorado

We are excited that the US community is looking to build an overview of available road centerline databases from (local) governments. We hope the ones we identified can help bootstrap this initiative.

We also published some MapRoulette challenges around this topic. 

ImproveOSM

Right on time for State of the Map, we launched a complete redesign of improveosm.org, our portal for everything Telenav❤️OSM. The new site gives you quick access to our OSM initiatives, data and tools. Check it out!We also released more than 20 thousand new missing roads locations. These are added to the existing database of currently more than 2.4 million missing road locations. An easy way to start editing based on these locations is to download the ImproveOSM plugin for JOSM.

Locations of the new Missing Roads locations

OpenStreetCam

The steady growth of OpenStreetCam continues. Almost 4.5 million kilometers of trips are in the OSC database. This amounts to about 165 million images!

We started a collaboration with OpenStreetMap US to run a Camera Lending program. Through the program, OSM US members can apply to borrow a custom Waylens Horizon camera for up to three months. The camera captures high resolution images for OSC and uploads them automatically. Almost 20 mappers have a camera already, and they have driven about 30 thousand kilometers in the past couple of months!

The passenger’s seat of our Camera Man ToeBee, as he gets ready to dispatch a bunch of Waylens cameras

That’s a wrap for our summer dispatch folks! Thanks for reading and keep an eye on the blog for more from the Telenav Map Team. Be sure to follow us on Twitter as well @improveOSM and @openstreetcam. 👋🏼

 

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New version of OpenStreetCam JOSM plugin with sign detections

This post also appears on my OSM diary.

The Telenav OSM team just released a new version of the OpenStreetCam JOSM plugin. The major new feature is the ability to show and manipulate street sign detections. Images in only a few areas are currently processed for sign detection, so it’s not very likely that you will see anything yet, but that will change over time as we catch up processing over 140 million images.

screen

To enable detections, right-click on the OpenStreetCam layer in the Layers panel, and check ‘Detections’ under ‘Data to display’. You can filter the detections by the following criteria:

  • Not older than — show only detections (or images) from that date or newer.
  • Only mine — show only detections / images from my own OSM / OSC account.
  • OSM Comparison — show detections based on comparison with OSM data:
    • Same data — Only show signs that have corresponding tags / data already mapped in OSM
    • New data — Only show signs that do not have corresponding data in OSM and need to be mapped
    • Changed data — Only show signs that have existing tags in OSM but the value is different (for example a 50 km/h sign and the OSM way is mapped as 60 km/h)
    • Unknown — No match could be made between the detected sign and OSM data
  • Edit status — show detections based on manually set status of the detection:
    • Open — new detection, status not changed yet
    • Mapped — manually marked as mapped
    • Bad sign — manually marked as a bad detection
    • Other — other status
  • Detection type — show only signs of the selected types.
  • Mode — Show only automatic detections, manually tagged detections, or both.

For the filters OSM Comparison, Edit status and Detection type, you can select multiple values by using shift-click and command/ctrl-click.

In the main editor window, you can select a sign to load the corresponding photo, which will show an outline of the detected sign. If there are multiple signs in an image, you can select the next one by clicking on the location again. (This is something we hope to improve.)

panel

In the new ‘OpenStreetMap detections’ panel, you can see metadata for the detection, and set the status to Mapped, Bad Detection, or Other. By marking signs that are not detected correctly as Bad Detection, you hide them from other mappers, and we will use that information to improve the detection system.

The plugin is available from the JOSM plugin list, and the source is on Github.Facebooktwittergoogle_plus

Detecting Traffic Signs in OpenStreetCam

OpenStreetCam’s mission is to help you improve OSM with street-view imagery. Photos taken with regular smartphones seem to be good enough for capturing map features like traffic signs, lanes or crosswalks. However, browsing the 120 million+ photos in OSC to find relevant things to map will take a while. The human factor is fundamental to OSM’s culture and we don’t see that changing, but we want to make editing street related attributes more efficient with automation.

We’re happy to announce a beta release of the traffic signs recognition on OpenStreetCam photos, made possible with machine learning. We processed a few million photos and detected around 500.000 traffic signs so far, currently available for tracks in several areas in United States and Canada. We’re working on extending the training sets and optimize the processing so that the area’s soon expanded.

What’s new from a user perspective: the track page on openstreetcam.org will now show detected traffic signs when available:

There’s a preview list of all detections in the track, detection overlays on photos and, of course, filters. Filters might now get a rep as something really exciting, but we’re excited about one of ours: the OSM status. Here’s why: after detecting a sign we compare it to the corresponding OSM feature and check if they’re consistent. Based on that, filtering is available.

For a practical example, let’s take speed limits: Instead of manually cross checking every detection with the maxspeed tag in OSM, one can only review detections where presumably maxspeed is not set or the value’s different in OSM. Just tick the Need review in OSM box.

Here are a few more examples of trips that have already been processed with our sign detections.

What’s next?

We’re busy working on a few things:

  • Scale the training sets and pipeline to extend the supported areas.
  • Traffic signs integration in the JOSM plugin.
  • Tagging new traffic signs support in the webpage.

If you like what we do and want to help:

  • First and foremost, you can use detections to improve OSM. If you’re seeing detections on tracks check them out, see what needs reviewing in OSM and edit. You can open iD or JOSM to photo’s location straight from the webpage.
  • Help us improve the traffic signs recognition. There’s a chance you will find some bad detections. You can review them and flag whether they’re good or bad, see the two buttons above the photo. We’re adding those reviews to training sets to improve recognitions, so please play nice.
  • Help us add these detections to the iD editor as well.

Tip: you can navigate between detections with Ctrl/Cmd + right/left arrows and confirm/invalidate with Ctrl/Cmd + up/down arrows. Goes pretty fast.

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3D Scene reconstruction

From 2D images we can extract a limited range of information like width, height and color. These can be useful to determine the regions of interest in our images: street signs, lanes, or even roads.

However, for a more accurate detection, the depth perception
is crucial. Here comes the 3D reconstruction into play. Extracting a 3rd dimension, the depth, we can determine how far from the
camera the regions of interest are and consequently, their shape. This way we can distinguish the road from the obstacles (cars, pedestrians, curbstones) simply because we know that the road has an increasing distance from the camera while the objects have a constant distance (fig. 1).

fig. 1 Depth Map

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