Category: Uncategorized

Last week we had released a new version of the OpenStreetCam JOSM plugin. While we are continuously working on improving and fixing existing functionality, we also keep adding new and exciting features.

Map view improvements

This new release introduces a major improvement to the map view. For small zoom levels, we had adopted a similar visualization as in the case of the web and mobile OpenStreetCam applications. Instead of displaying individual photo locations, we display ways that have OpenStreetCam data coverage. Segments are colored with purple and have different transparency based on the data coverage: segments that have many images are opaque while the segments that have only a few images are more transparent.

By changing the initial MapView visualization we were able to display OpenStreetCam data starting with zoom level 10. This way we can indicate areas that have street view coverage at a country view level and possibly give a hint to the user where he/she can find an extra source of mapping support.

Starting with zoom level 18 the map view changes and individual photo locations are displayed similarly as in the previous versions of the plugin.

The displayed data type is user-configurable and can be changed from the OpenStreetCam plugin preference settings. You can access the preference settings from JOSM ->Preferences -> OpenStreetCam plugin -> MapView settings or from the OpenStreetCam panel by clicking on the preference icon. 

From the MapView settings section, you can change the minimum zoom level at which image locations are displayed, along with the data type change method. By default, the MapView data type is changed automatically.                                                                                               When the “switch manually between segment and image view” option is enabled a new button is visible in the OpenStreetCam panel.

The “data switch” button is enabled starting from zoom 16 and is represented with different icons based on the displayed data type. For segment map view a photo icon is displayed while for image location views a segment icon.

If you click on the button the map view changes from segment view to image location view and vice-versa.  The type of data can be changed manually for zoom levels bigger than 16.

Layer and panel improvements

The OpenStreetCam layer and panel default visibility had been improved and previous open/closed states are remembered for future JOSM sessions. After installing the plugin in order to see the OpenStreetCam data you need to open manually the layer and panel. The layer can be opened from the Imagery -> OpenStreetCam menu, while the panel from the left side JOSM menu.

We had changed the OpenStreetCam window button panel actions and removed the actions that were not related to the currently selected image. Feedback and filter actions were added to the OpenStreetCam layer menu:

In case you need a refresher:  OpenStreetCam data can be filtered based on date and currently logged in OSM user. Basically, you can visualize images that were uploaded after the specified date. You can also visualize only your contributed data.

Nearby Photos

An important feature that we have added to the plugin is the nearby photos functionality. This functionality improves the mapping process especially if the selected photo does not contain all the information or if the selected photo has bad quality or has not the right angle.

A nearby photo of a selected photo can be visualized either by clicking on the “Nearby photo” icon or by pressing ALT+N keys. 

If the “Load track on image selection” preference settings option is selected then also the track corresponding to the nearby photo is loaded.

Nearby photos are computed based on the currently visible photos, if the user moves the map or zooms in the set of nearby photos is recomputed.

A photo is considered nearby if belongs to a different track and it is located to the maximum distance from the selected photo.

Photo load on mouse hover

Another important feature that we had added to the latest release allows users to quickly load photos on mouse hover action.  By default this feature is disabled and can be activated from JOSM ->Preferences -> OpenStreetCam plugin -> Image settings.

If this feature is activated, then the small thumbnail image is loaded in the OpenStreetCam panel and remains loaded only it is explicitly un-selected from the map.

A better resolution image is loaded if you click on the image location icon or if the OpenStreetCam panel is maximized.

Upcoming features

The JOSM plugin is a work in progress, we are working on improving the usability and plan to add new features from time to time.

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!

The OpenStreetCam JOSM plugin helps the community to improve the map by displaying up-to-date street view images. Street view images are collected by the OpenStreetCam platform and are available also via the OpenStreetCam web application and map editor.  

Having an extra source of free and open imagery eases the process of remote mapping and allows the users to reflect the reality also on the map. Street view images are helpful for editing map features that are not visible on satellite imagery like traffic signs, house numbers, bus stops, points of interest.

Installation

Install the OpenStreetCam plugin the familiar way, through the JOSM plugin Preferences menu item. After you install the plugin and restart JOSM, you should see the OpenStreetCam layer and panel.

OpenStreetCam layer

After a successful installation, the OpenStreetCam layer is available in the layer menu panel, and on the main map, the image locations are displayed. Image locations are illustrated with blue icons, each icon indicating the image heading.

An image location can be selected by single mouse click action as long as the layer is visible. You can select images even if the OpenStreetCam layer is not the active layer.

OpenStreetCam layer displays data starting with zoom level 14, so in order to see the data, you need to zoom in into the desired mapping area.

For Imagery layers, the data is loaded as you move the map and zoom in/out. In the case of OSM data, the OpenStreetCam layer data is loaded only for the downloaded area.

The plugin saves the open/closed state of the layer. So if you delete the layer then at the next JOSM session the OpenStreetCam layer will not be loaded by default. A previously deleted OpenStreetCam layer can be activated again from the Imagery menu.

OpenStreetCam panel
In the OpenStreetCam panel, you can interact with the currently selected image.

The panel along with the image displays basic information such as OSM username and date of creation.

The panel also has a number of action buttons on the bottom. These are for filtering, next/previous image loading, centering the map, opening the image web page, and giving feedback. Image-related actions are enabled only when the image is showing in the panel.

These features will be discussed in the next sections.

The plugin saves the open/closed state of the panel. So if you delete the panel then at the next JOSM session the OpenStreetCam panel will not be opened by default. If you don’t see the panel you should be able to open it by selecting the OpenStreetCam icon from the left side panel.

Image filtering

The displayed data can be filtered based on the creation time and JOSM user. In order to view only your uploaded images, you need to authenticate in JOSM using OAuth login.

 

By default no filter is set, custom filters can be removed by clicking the Clear button.

Visualizing an image and corresponding track
Individual images can be visualized by clicking on the image icon displayed on the map. The corresponding image is loaded in the OpenStreetCam panel and the corresponding track is displayed on the map.

An OpenStreetCam track is illustrated with a blue directed line. Images belonging to the selected track are illustrated with opaque icons; while other images along the track are illustrated with transparent icons.

Image zoom in/out

The displayed image can be zoomed in and out using the mouse wheel. In an already zoomed-in image details can be observed by moving the image left, right, up, and down.

Next/Previous image

You can navigate between the previous and next image of a track either from the OpenStreetCam panel by clicking on the Next/Previous button or by pressing the Alt-Left arrow/Alt-Right arrow.

If the next or previous image is not visible in the current view, the map is moved automatically and images near the track are downloaded.

Center map to the selected image

The map can be re-centered to the selected image location by clicking on the “Location” button from the OpenStreetCam panel. This feature is useful when the map was moved and the selected image location is not visible on the map.

Image web page

The selected image web page can be opened by clicking on the “Globe” button from the OpenStreetCam panel.

Upcoming features

We are working on improving our JOSM plugin and plan to add new exciting features. In the near future we plan to:

• improve image loading speed by adding caching mechanism
• allow the user to select easily nearby images to an already selected image
• improve the map view and suggest street view coverage by displaying OSM ways instead of individual images. We will implement something similar as in the case of the web and mobile applications.

Source code

The source code for the plugin can be found on GitHub.

Feedback

Ideas, suggestions, and bug reports can be submitted either to the plugin’s GitHub issue page or to the Feedback forum. Other mapper’s ideas can be voted there.

We take a look at all incoming ideas, so be sure your input is heard and very much appreciated!

Have fun adding missing map features using OpenStreetCam images.

One of our main goals here at Telenav is to constantly improve the maps we are using in our applications and services Having very detailed and accurate maps is of fundamental importance if we want to build high-quality and precise routing applications, ADAS systems, or self-driving guidance software. In this post, we’re going to talk about how we leveraged our massive datasets of anonymized GPS (probe) data in order to enhance the OSM maps, more specifically how we were able to detect missing roundabouts throughout the world.

The Task

The problem at hand is as follows: Given a dataset of GPS probe data and the current OSM geometries, could we identify missing roundabouts? More precisely, we are searching for geometries that lack a specific tag ( junction = roundabout) identifying them as such.

A relevant case would be the one below, where the geometry clearly defines a roundabout, but that specific tag is missing from the OSM map.

What we decided to do is to analyze car movement patterns and use this analysis to make inferences about the underlying map topology. In order to solve this not-so-trivial problem, we decided to harness the power of Machine Learning. We did this because we are aware of the huge recent developments in this field and of the powers of a well-designed Machine Learning algorithm when combined with huge datasets. 

The Solution

The intuition about why this approach is preferable is obvious when analyzing the available data and how different traffic patterns are when we are in the context of a roundabout compared to the context of a normal intersection. What we have achieved is to teach the algorithm to associate the circular traffic movements having a “hole” in the middle with a roundabout and to associate the evenly spread movements with a normal intersection.

    

After successfully developing this “smart” detection algorithm, we have selected from the world map approximately 117 000 potential points, where a roundabout would be likely to exist based on some predetermined criteria. Of course, these are far too many to manually check, so the automated solution is the only one suitable for this job.

The Results

After running those points through the Machine Learning algorithm, it has detected around 9000 missing roundabouts in Europe and North America, as those are the areas for which we have GPS probe data. The massive size of these results which translate to substantial improvements of the OSM map is obvious when visualizing them.

After a quick series of manual testing of a batch of results, we have discovered that the predictions are between 82% – 86% correct, depending on the level of confidence, which proves the efficiency of the Machine Learning oriented solution to this difficult task.

What’s next

In the near future, we plan to release this data to be validated by the OSM community through the MapRoulette platform and we are eager to see the feedback we get. Having acquired even more knowledge in this field, we are now ready to tackle more difficult problems using more advanced Machine Learning and Deep Learning algorithms. This will surely enable us to improve the OSM maps even more.

PS

We have uploaded all our predictions here in CSV format for those of you who are interested in playing with the data.

We have over the last 12 months starting to look extensively into how we can leverage AI / Deep Learning to help improve OpenStreetMap and today we want to provide a few details about how we envision the future of making maps and also share more on what we are already doing. We see the emergence of self-driving vehicles as a game-changer and one key requirement for those vehicles is accurate and up-to-date maps. Currently, commercial map providers map every region around every 12-24 months – in a costly process with high precision and high-cost vehicle, our goal was to achieve maps that are updated on a minute basis and with key streets covered at least once every day. This is the goal we set out to solve with OSM in supporting to make it ready for this use case.

Using OSM for Navigation Maps

At Telenav (and before at skobbler) I’ve been actively involved in OSM for almost 10 years now and it is truly unbelievable how OSM has grown massively in that period from a map that was used mostly by passionate enthusiasts to a map that is used by 100s of millions of users and big companies such as Toyota, Tripadvisor or Apple to just name a few to power their consumer products. Despite this success, we have still seen that for navigation maps many additional attributes are needed that are not that well covered in OSM such as Signposts, Speed limits, Turn restrictions or Lane Information is needed to provide the best possible guidance.

Speed limit coverage

Turn restriction coverage

Turn restriction coverage in the United States

What we have done especially to close the turn restriction gap is to use (anonymized) GPS probe data from our millions of collaborators and from partners like Inrix to detect where there are likely turn restrictions based on turn behavior. This data is then shared with the community via ImproveOSM and also for the most likely cases, we put a high penalty on turns for our users so they avoid those maneuvers if possible. This way we have been able to detect 139,181 turn restrictions and increased coverage in a meaningful way.

Next step: Higher accuracy with Computer Vision

With Speed Limits, Lanes, and Sign Posts it is significantly more tricky as it is not possible to identify those purely from GPS probe data. This is the reason why we started our OpenStreetView project to capture those images as there was no truly open project for Streetlevel Imagery that we could use (when we approached Mapillary they asked for hundreds of thousands of dollars in license fees – which was not an option for us).

In parallel to the OpenStreetView projects, we have invested a lot in Computer Vision algorithms and established a cooperation with the Technical University in Cluj to get there over 15 years in the field. Our goal was to use computer vision to automatically build maps based on these images.

In the last year, we made very significant progress, and now we are able to detect Speed Limits, Turn Signs, and Signposts (incl. OCR the text in those signs). Those detections when made will be reviewed by our editors and added directly into OSM.

<Slideshow with our computervision images for detecting turn signs, OCR, speed limits>

Input picture

Panel detection

Glyph segmentation

Character grouping into words

OCR and classification results

We have to build a map editor that allows us internally to review those changes and add them with our team of 20+ mappers to OSM.

We have by now added 19,798 map features (turn-restrictions,one-ways, signs) to OSM using this tool, and are adding every week hundreds of new turn restrictions and other signs to the map to make it better.

<MAP EDITOR TOOL SCREENSHOTS>

Map editor tool

Map editor tool

Advanced level: Create High Accuracy maps (ADAS / HD maps)

The next level for this challenge was to create the high-accuracy maps needed by self-driving cars and for ADAS (Advanced Driver Assistance System) applications. Those maps need accuracy < 2m which typically OSM doesn’t provide consistently and which is a big challenge to achieve purely based on GPS probes as we learned through a lot of trial and error. We looked into how we can achieve better accuracy and our natural choice was to leverage car data that is available to achieve higher accuracy. Therefore we integrated our OpenStreetView application via an OBD2 port (which is available on every car manufactured in the last ~20 years) to integrate our phone-based data with data coming directly from the car (such as speed, or on some models even with steering wheel angle available via OpenXC). With this, we have been able to achieve an accuracy that is 5-10x higher than purely achievable by Phone-based GPS, and with several passes on one road, we can create truly high accuracy maps. P ENHANCEMENTS FROM HARALD>

Trip Enhancement

Our vision of the future of map-making:

We believe if enough users help to record the necessary images via OpenStreetView maps can be created in near real-time at unprecedented accuracy. This would be a major enabler for self-driving cars and update navigation systems. In order to make that possible, we are also in the early stages of working with several car manufacturers to use the data from their onboard cameras in the future for those detections, and hopefully, this way we can use millions of cars from our OEM partners in the future with this technology to enhance maps and share this data with the OSM community to create even higher quality maps than today.

We will over the next few weeks go into this blog deeper into the individual modules that we built for making this future happen and looking also forward to feedback from the community.

<TEAMPICTURE OF TELENAV OSM TEAM>

OpenStreetView team

This is a guest post by one of our Map Analyst team interns, Manuela.

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While editing the map I stumbled upon clusters of orphan nodes. Basically, orphan nodes have no tags and are not part of a way. One example here:

Some online tools report these as bugs/issues (e.g. osmose). You should be careful, though. These may have been created for a reason. Before proceeding to deletion, ask yourself:

• Were these nodes orphan from the very beginning?
• Is there just one orphan node in a changeset or are there many?
• Are they arranged in a special way/shape?

If so, they may be GPS traces that could be used for mapping. And still, even nodes from GPS traces should be deleted, there is no reason to keep orphan nodes in the map AFTER you extracted all the needed information from them.

My advice: research! There are many tools to find out the history of an OSM element. To name a few: object history, WHODIDIT, OSM History Viewer, attic data, etc.

You’ll probably find yourself in one of the following situations:

• You’ve found a newbie that creates orphan nodes by mistake
• The nodes are correct but the user forgot to add the tags
• A Redaction bot deleted ways without deleting the corresponding nodes (read more here)

Your options:

• Contact the creator or the user who made the last change via a changeset comment or private message (in a friendly way, of course)
• Add relevant tags if that’s the case
• Delete the nodes or leave a fixme tag for other users that may know the area better than you

How to find orphan nodes

Osmose will report orphan nodes clusters as issues:

In JOSM, orphan nodes (even isolated ones) can be easily found using the Search tool, with the following queries:

type:node tags:0 -child

or

type:node untagged -child

The -child tells JOSM to select only those nodes that are not part of a way.

As if that wasn’t enough already, I’ve created a map paint style that highlights orphan nodes and dims other elements. This will help you analyze the distribution of the orphan nodes without needing to select them and will help you make the decision to delete or no.

If you want to use this map paint style, you can find the script and a step by step guide on the GitHub page:

https://github.com/manuelabutuc/JOSM-Orphan-nodes-map-paint-style/tree/master

Have fun spotting orphan nodes! But remember to delete them only if you are sure they have no reason to be on the map. If you have any improvement suggestions for this map paint style, feel free to comment below or fork the project.

On Internet Day, May 17, 2016, the President of Mexico Enrique Peña Nieto invited fifty citizens who called Digital Leaders (#LideresDigitales) to have a dialogue on the future of technology and the Internet in Mexico, I had the opportunity of being among this group of citizens. The President talked about various related topics but especially appreciated the efforts of humanitarian mapping conducted by Humanitarian OpenStreetMap in Hurricane Patricia.

Why the President of México thanked the efforts from HOTSOM in Hurricane Patricia? Here you will find some details so you know what was done.

On October 23, 2015, Hurricane Patricia threatened to touch the states of Colima, Nayarit, and Jalisco with winds up to 325 km/h, authorities of Mexico mentioned “It is very likely that this hurricane is the strongest ever in the Pacific side of our country since it has recorded”

Contributors of OpenStreetMap and HOTOSM like Rodolfo Wilhelmy, Humberto Yances, Rafael Avila, Robert Banick, Andres Ortiz, and many others (sorry for not mentioning everyone)  in addition to an army of over 500 mappers of Mexico and the world joined efforts to support this area of the Mexican Pacific with data that could be used for the benefit of the population that could be affected. Fortunately, the hurricane lost strength by touching the coast of Mexico causing minimal damage compared to what was expected.

Quick stats:

• More than 500 contributors mapped 9,000 km of roads (5.6k miles of road) + 72,000 buildings in 72hrs
• It was processed 29,608 km^2 pre-event DigitalGlobe imagery to improved coverage over priority areas.
• It was analyzed INEGI road data to identify missing roads and road names in OSM data.
• Mexico Open Data was confirmed by authorities to be used in OpenStreetMap.

All these were possible thanks to the great work of HOT members, companies supporting the OSM project, and the local community in Mexico and the World

The event took place in Los Pinos (The equivalence of the U.S. White House) at the moment the Open Data topic was mentioned, Peña Nieto said he knew someone who had supported the alert for Hurricane Patricia was among the guests so I raised my hand to start the dialogue, the President mentioned: “… I just want to thank because it’s an example that illustrates very well what we can achieve and I think that you also use open information.” In my participation, I could give my point of view on the need for Mexico not only to upload open data to be the first in the quantity of released Open Data but emphasize the need for quality Open Data in order to make better decisions based on them. Also, I could mention the importance of Open Mapping and collaboration between Governments and Civil Society so more Mexicans are less harmed by disasters (find the video here).

In Mexico, the OpenStreetMap community is not as numerous as in other countries but in the last two years a group of collaborators we have joined together to promote the project and increase the local community through massive workshops in Universities and courses for Government Authorities and Civil Society. Much remains to be mapped but I believe we are on the right track.

Miriam Gonzalez

@mapanauta

Last April 6th 100% of the Mexico Telenav’s team (Andrés Ortiz 50% and Miriam Gonzalez 50% 😀 ) presented the results of Import Project INEGI National Geostatistical Framework. The meeting point was the Felina bar on the edge of the Condesa and Escandon neighborhood.

More than 20 people booked and came to the appointment. The project was originally announced in May 2015 with much skepticism because this was the first time a project of such magnitude was taking place in Mexico and the OpenStreetMap community in Mexico, at that time very dispersed.

Many import projects have been conducted in many parts of the world, such projects have helped (mostly) to create the map of the world that we have today and Mexico was going to be part of them. People with extensive knowledge in imports formed part of the project including Victor Ramirez, Ernesto Carreras, contributors of OpenStreetMap Puerto Rico, and Rafael Avila, a HOTOSM collaborator and expert in African countries imports. At the beginning of the project, we realized that there were only 69 valid administrative boundaries (although in the image it looks more than 69,  these lacked the tag SOURCE which made them invalids) and at the end of the Import project the team had added 2,457 administrative boundaries with tag Source = INEGI MGN 2014 v6.2

To the #HappyMappingHour diverse OSM contributors attended such as geographers, developers, archeologists, and also Armando Aguiar – INEGI IT Services Director witnessed how the Open Data Inegi released at the end of 2014 has been in benefit of OpenStreetMap. Let me share some statistics:

Quick Statistics:

Node numbers/Ways/ Deleted relations

• 500K / 2k / 500

Node numbers/ Ways / Added relations

• 1000K / ~4k / ~1050

Number of hours dedicated :

• 250+

Number of administrative boundaries added:

• 2,457

Now that the map has de MGN boundaries as a reference mappers as Irk_Ley have been investigating the local laws of the states of Veracruz and have been reviewing historical maps of the Map Library Manuel Orozco. These mappers will be verifying and correcting those limits which have differences with the MGN when they have the backup of the documentation of the local law.

Here you will find the presentation of #HappyMappingHour and if you want more technical details we suggest you check the following blogs and the wiki.

• Blog-Process used to import more than half of the municipalities in Mexico 
• Blog-How we imported Administrative Boundaries for Mexico from INEGI
• Wiki- Mexico’s Administrative Divisions Import Project

Here also you will find two Blogs from collaborators in the Import Project:

Blog: My experience in OSM during the MGN Import by Pablo Garcia (OSM user: Irk Ley)

Blog: Import of INEGI Mexico municipalities finished by Andres Ortiz (OSM user: Andresuco)

You can contact them directly if you have any questions or comments for them.

What are the next challenges?

Evaluate data from the National Road Network and create a joint project with the Mexico OpenStreetMap community to carry out its import. It is also in the radar create a tool where information from OpenStreetMap in Mexico is a kind of “inspector” to send feedback to INEGI about possible shortcomings or errors that can be corrected and improved thanks to contributors OpenStreetMap but first we need more discussions with the local community.

One of the biggest challenges when working with large sets of data is to find the least costly workflow that you have to follow in order to get the most accurate answers.

Let’s say you have a huge dataset composed of all sorts of geometry features (points, lines, areas, etc.) and you want to do a bit of cleaning – because messy and redundant information is no fun!

So you might be thinking “Hmmm… which are the areas that have an unnecessary high density of points?”

The same issue can arise when working with OpenStreetMap data. This can be easily solved using PostGIS and a command-line tool that we’ve created and used.

Note: The following steps require a Linux environment, Postgresql 9.x, PostGIS 2.x, Osmosis 0.43+, QGIS 2.12.2+

Getting the data

Download a *.osm.pbf file using the command line:

 wget https://s3.amazonaws.com/metro-extracts.mapzen.com/san-francisco_california.osm.pbf

This is the metro extract for San Francisco, provided by Mapzen. Geofabrik is also a very good resource for OSM data extracts.

In the same folder, download SCOPE – databaSe Creator Osmosis Postgis loadEr.

wget https://github.com/baditaflorin/osm-postgis-scripts/blob/master/scope.sh

Make sure to set the file to be executable by using

chmod +x scope.sh

Load the data

Using SCOPE and following the instructions on the screen, load the *.osm.pbf into a database.

SCOPE automatically creates the database with hstore and PostGIS extensions and the pgsnapshot schema.

Play with the data

Now that you have the data set up, you can easily query it using the DB Manager from QGIS and some PostGIS scripts.

Interesting examples

For example, using the find_duplicate_nodes query, we can see that this building (@20.805088941495338, -104.92877615339032), appears on the same spot 23 times!

The one next to it (@20.8054225, -104.9278152) appears 22 times!

The node density for these areas (@20.4411867, -97.3172739) is too high – 168 nodes!

Also, 171 nodes for a small fence segment (@46.7487683, 23.559687)!

Feel free to fork the GitHub repository and modify the code to suit your needs! Also, if you feel inspired, you can suggest a better and shorter name or acronym for SCOPE!

On the 17th of April, we had our first Mapping party event for this year. Our main focus was to improve the map of our hometown by reflecting on the latest changes. Cluj-Napoca is a dynamic city, many new buildings were constructed; POIs, turn restrictions, addresses have been changed and appeared since the last field mapping.

Around 30 map enthusiasts show up on Sunday morning for the Mapping party. There were both experienced mappers and newbies present at the event. The event had started with a morning coffee and some instructions regarding data collections.

For data collections we used the following tools:
• Field papers: our colleague Florin Badita had taken some time before the event and had created field papers for several city areas

• GPS tracker applications: OSMTracker, OSMAnd, Pushpin OSM and so an
• OpenStreetView application 

We have divided the people into smaller groups of 2-3 persons. After each group had chosen an area to map we went out to collect the data.
In the afternoon we headed back to our meeting location to add the collected data into OpenStreetMap.

An outcome overview of our mapping effort is presented on the following images: