Australia to Vanuatu: A visual exploration of the mapping challenges in the Asia Pacific region
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Neerhut, Edoardo
Australia to Vanuatu: A visual exploration of the mapping challenges in Asia and the Pacific
 My name's Eduardo, I'm going to talk to you about Mapillary, a platform for street level imagery that's used to derive map data. I've also got my colleague Mutu in the audience, he's also from Mapillary, he's going to be helping out. So I have a few slides embedded within this presentation that are going to show you a place in Oceania. And whoever can guess that place will get some swag from us. If you missed out, there's also an opportunity to come to our table later. The thanks for coming all the way to Australia, for those who travel from Farwin-Rye, thanks to those in Melbourne who've made the travel to our lovely Parkville campus. This is an unwieldy name, Phosphor G, said in Map Oceania, but I think it represents two important communities that have obviously overlap, the open street map community and the Phosphor G, which is more usually tied to QGS and R and packages like that. But I'm here to talk to you about Mapillary and how it applies to the Pacific, in particular Oceania. So I gave this kind of a similar talk in Asia recently and asked the same question, like what does Asia mean? It was this very broad term, it's nebulous, it's got countries like Turkey and Japan that have about as much in common as Japan and the United States do. I think the same thing can be said of Oceania. We are the misfits, the countries that kind of don't belong to any continental landmass. What do we have in common? What kind of learnings can we share amongst each other? What are the differences? How are we solving GEO spatial problems in our region? So I want to share with you some GEO spatial problems from the Asia Pacific, from a few different places. But before I do that, I'll talk to you about Mapillary and what it is we do. So we are essentially a platform for images, as I said, from which map data can be derived. When I was preparing this presentation, or actually the brief for the presentation if you read it, I said that by this date we will have surpassed 370 million images on our platform. And I'm happy to say that I had to update the slides a few days ago because we smashed past that 401 million images. And it's because of people like you that are contributing to the platform. That's how we work. So a round of applause to everyone who's been contributing imagery. All this data is going into OpenStreetMap. So for those that just came in, I was prompting you, what is Oceania? How can we learn from one another? What are the differences that we have as far as the GEO spatial challenges? I talked about Mapillary and how we're a platform for street-level imagery. We derive map data from that imagery. And I also started speaking about why is it that this approach, having people contribute with cheap Android smartphones all the way to hiring equipment, why is that the way to do it? And that that was more cost-effective. It allows us to go to places that we otherwise wouldn't get to go and to scale in a far more cost-effective way. And so the kind of devices that we're talking about are everything from, as I said, a smartphone, through to the professional rigs that you might see. Not just Google having on the road, but a number of companies, even your own local council might be paying for that kind of collection. And it costs a lot of money and often doesn't come to that area. They don't get the imagery for years afterwards. So the second aspect that I mentioned was, I think, an advantage of street-level imagery, particularly on the Mapillary platform, is computer vision. And so what you see here is data that we've collected and then processed. And so every image you upload to our platform is being processed. We have semantic segmentation taking place. So this is where we identify at a pixel level what that pixel is. So in this case, cars. You might have seen the lanes detected. And this is a drive down, I think, at Arts in Austria, where our computer vision team is based. And as far as map data, so lanes are obviously very important for anyone who's working with map data related to roads. There's traffic signs, which give us speed limits, location by planes, things like that. And then underlying all this is a 3D point cloud. So we're identifying points that we know are the same across different images, reconstructing that. And that allows us to know where your photo was taken a bit better than we otherwise would, particularly if the GPS is bad. But also to identify objects like this. So there's trash cans. We've got utility poles. All things that a government or a citizen might be concerned about and want to add to the map. So at Mapley, we're not adding map data to OpenStreetMap. We're not a mapping company per se, but we create this data and we make it available. And all of this data is free and open for OpenStreetMap. So this was what you're looking at before. No one got the islands yet, so just keep that in mind for those that didn't see it. It's an island somewhere in New Zealand. But I want to talk about a history with OpenStreetMap. We as a company were founded in October 2013. And then since then, we kind of got involved with OpenStreetMap in the very early days. So the first interaction was my colleague Peter, this crazy, awesome German guy who started hacking on ID Editor, which is one of the editing tools for OpenStreetMap. He put Mapley imagery into that tool. And then shortly after that presented the first, or at his first added map in Argentina. And that was where the critical momentum grew. All these people who are editing the map and needed some sort of visual reference point could now use Mapley to do that. And since then, we've had integrations in Josson, which is another tool to edit the map. Traffic signs were added. So all this computer vision work is actually available now in OpenStreetMap. And then the most recent thing is up in the top right is we're seeing these awesome tools built by other people that utilize our API. And these tools, a lot of OpenStreetMap tools now are more tasking orientated. So you have companies, but also individuals coming in. And rather than like this general editor that has specific tools to address problems like roads that might be missing. Or actually, I'll give you a few examples now. So the first one is pick through review. And this was the kind of the brainchild of Adrian Pabdi, a guy in France. And he mentioned it to me in Japan in August 2017. And it was one of those things at these conferences. You have a lot of like great talks, you get fired up. But a lot of the time, those talks don't really turn into anything. So he said to me, Ed, I'd love to incorporate imagery into this tool I'm building. And I thought, OK, that's awesome. I hear that a lot. But the great thing about Adrian, within a few months, he'd actually made the first version of this. So straight to the action. And now there's this tasking manager, which essentially anyone can create a task. And you can say, look, I've got images in these areas. And I want you to help me identify which crosswalks are around here. And how suitable are they for wheelchairs to get up and down off the sidewalk? This one here is the number of lanes. So you can quickly get images and then tell them or tell OpenStreetMap how many lanes are there. And rather than having to go into Idea Editor or Jocelyn and edit them up, it's a really easy tool that anyone can use. It's just a visual reference using Mapillary imagery. The other thing that might be of interest to all of you is a plug-in. Again, we had nothing to do with this. This was developed by a guy called Enrico in Italy. And this is a QGIS plug-in that allows you to see imagery alongside your existing QGIS feature set. So it's kind of work in progress at the moment. But it's the first chance for you to view Mapillary imagery in QGIS. And if you're an ArcGIS user, you can do that too with our own plugins. So what are some of the challenges in the region? The first one I want to share with you is in Laos. This is, I think, a pretty cool project that the World Bank has been organizing. So Laos, even if you look at this map, it's an incredibly difficult country to govern because it's landlocked. And running through it is the Mekong River. It's got mountains. And a lot of effects from climate change. So roads can get washed away. Trees can fall over on the road. And when those roads are remote, that's really hard to maintain. So the World Bank, supporting Laos in investing funds in the country, which is one of the fastest growing economies in Asia, is looking at how can we maintain roads better. And so you can see here, like, this is Mapillary coverage that they've started to get. And they're looking at, like, working out what the quality of the road is now and then keeping regular survey of that. So I think they've got about 2,000 kilometers of roads that they maintain now on a regular basis, and then it might be another 1,000 that they do on a more intermittent level. But they use imagery to kind of get a sense of how the road is over a month to month basis and start to build an idea of, like, what are the hotspots which need assistance more often. And one of the tools they're using is Complete Map. And again, this is something anyone can create. And it allows you to track how much imagery is in an area. So it works out, it uses OpenStreetMap to calculate how many roads are in that area. And then we'll tell you what percentage of those roads you've covered in a color coded way. So the government employees are using that. And the last point here, this is really cool. This is tagging. So anyone can go in and tag objects. So this is kind of, I guess, what we call dumb tagging. It's just, like, literally an image and then some text associated with this particular point here, which is the pot hole. But what we're also doing is trying to do smarter stuff with tagging. So we've got, like, a verification tool whereby anyone can tell us whether or not we've correctly identified an object. And that feeds back into our machine learning algorithms that I showed before so that we get better and better at traffic sign recall. So there's also tagging that's going to come on later where you can actually annotate objects yourself. And that'll be a real, I guess, shift, or greatly increase the number of objects we can identify. The other example from the Asia Pacific region, this is Chiang Mai. So many of you might have been there. It's a very popular expat city. But the map is quite blank. There's not enough, as there should be, features, not like some other cities around the world. So one guy, he's been capturing pretty much the whole city on his motorbike. He's got a little scooter. He's also got a car. His name's Johnny Carlson. And so what he does, he goes on family road trips. And then he'll be coming back from the family road trip and he'll say, honey, is it all right if I take a left here? This street isn't covered yet. Just like, all right, Johnny. That's all right. We can do that. Next trip, can I take a right here? And the trip started getting longer and longer. The detours before they got back home. So I heard from someone who visited Johnny that his wife was getting increasingly annoyed about this. I think they're still together. So that's all right. But we have a lot of coverage in Thailand now. Thanks to him. And what are we doing with, or what's he doing with this coverage? He's adding points of interest to the map. So things like the shot fronts you identify here. I mean, I wouldn't be able to add to that, but anyone that speaks Thai would be able to get a wealth of information from that. And that translates to POIs, so points of interest like restaurants and cafes. So I mentioned kind of like World Bank. I've mentioned an individual contributing. And then there's also what we're kind of working on now. So this is my colleague, Ryan, coordinating, capturing us. And when a government wants capture and wants to do it more cheaply and more quickly, Ryan has kind of put together a method to do this. So this is Portland in Oregon. And so in 10 days, the city of Hillsborough had their entire municipality mapped. And then within the month, all of Portland was mapped. And that was just with drivers who like Uber drivers or Diddy drivers, or they're just like that flexible income that they get from being able to work when they want, in this case, as long as it's daylight. And they get equipped with four cameras. And these cameras capture high resolution imagery, which is exactly what the government needed in this particular use case. And this use case was identifying traffic signs. They had their existing database of traffic signs. And they wanted to see, what are we missing? And so when Mathillary identified a sign that they didn't have, they could put that in their database. This was, anyone remember? Yeah, that's right. Four minutes left. OK, I want to announce something. Camera Grant Program. So if you're thinking about capturing from Mathillary, maybe you don't have the camera you need. We're trying to adjust that with a camera grant program. So if you qualify for a camera grant, we'll send you out a camera to yours to keep. And we're kind of sending out three types of camera that we've identified and useful for the majority of scenarios. So one is a dash cam that you see here, a high quality dash cam. You just put it in your window, kind of forget about it. Make out the SD card and upload when you're ready to do that. The second is a GoPro, the new GoPro. And then we have that with a setup for cars and then a setup for bikes. So if you're on a motorbike or a cyclist, you can get that setup. If you drive a lot, you can get the car kit. It began as a humanitarian initiative. So we had a lot of people in Tanzania and other African countries who needed a camera. But then we realized there were people around the world who needed cameras to capture imagery. So that's why we've got the program. And I encourage you to apply it when it opens up, I think probably the end of this week. Addressing three scenarios, making the quality of imagery better, increasing the amount of imagery you can capture, and also allowing you to go to places with a camera. So if you're riding a bike, it might be hard with your smartphone. Now you have a GoPro to do it with. So I was lucky to capture New Zealand a few weeks ago. Very easy to do, chuck this magnetic mount on the rental car, damage the bonnet a bit, but luckily they didn't catch that. But I can keep a charge as I go, which is excellent. And I captured a lot of roads in beautiful New Zealand, need an excuse to go back. So as we depart, I encourage you to be like Adrian, that French guy I mentioned, who turns the torque here into action. I encourage you to drive off into the sunset with the GoPro on the front, maybe even the back, if you're as keen as we are, and capture a lot of imagery, but don't do it at night, because we hate that. And last but not least, actually I'll skip this one. Where is this? Tasmania. Yeah, how about Kassie? Yes. This is by one of our top contributors in Tasmania who I hope to meet. Is he in the audience? Yes, he's up the back. Excellent. And this is Tas Tracks as he lives and breathes. He's captured a lot of imagery in Tasmania. So I'll leave it with that.
 My name's Eduardo, I'm going to talk to you about Mapillary, a platform for street level  imagery that's used to derive map data.  I've also got my colleague Mutu in the audience, he's also from Mapillary, he's going to be  helping out.  So I have a few slides embedded within this presentation that are going to show you a  place in Oceania.  And whoever can guess that place will get some swag from us.  If you missed out, there's also an opportunity to come to our table later.  The thanks for coming all the way to Australia, for those who travel from Farwin-Rye, thanks  to those in Melbourne who've made the travel to our lovely Parkville campus.  This is an unwieldy name, Phosphor G, said in Map Oceania, but I think it represents  two important communities that have obviously overlap, the open street map community and  the Phosphor G, which is more usually tied to QGS and R and packages like that.  But I'm here to talk to you about Mapillary and how it applies to the Pacific, in particular  Oceania.  So I gave this kind of a similar talk in Asia recently and asked the same question, like  what does Asia mean?  It was this very broad term, it's nebulous, it's got countries like Turkey and Japan  that have about as much in common as Japan and the United States do.  I think the same thing can be said of Oceania.  We are the misfits, the countries that kind of don't belong to any continental landmass.  What do we have in common?  What kind of learnings can we share amongst each other?  What are the differences?  How are we solving GEO spatial problems in our region?  So I want to share with you some GEO spatial problems from the Asia Pacific, from a few  different places.  But before I do that, I'll talk to you about Mapillary and what it is we do.  So we are essentially a platform for images, as I said, from which map data can be derived.  When I was preparing this presentation, or actually the brief for the presentation if  you read it, I said that by this date we will have surpassed 370 million images on our platform.  And I'm happy to say that I had to update the slides a few days ago because we smashed  past that 401 million images.  And it's because of people like you that are contributing to the platform.  That's how we work.  So a round of applause to everyone who's been contributing imagery.  All this data is going into OpenStreetMap.  So for those that just came in, I was prompting you, what is Oceania?  How can we learn from one another?  What are the differences that we have as far as the GEO spatial challenges?  I talked about Mapillary and how we're a platform for street-level imagery.  We derive map data from that imagery.  And I also started speaking about why is it that this approach, having people contribute  with cheap Android smartphones all the way to hiring equipment, why is that the way to  do it?  And that that was more cost-effective.  It allows us to go to places that we otherwise wouldn't get to go and to scale in a far more  cost-effective way.  And so the kind of devices that we're talking about are everything from, as I said, a smartphone,  through to the professional rigs that you might see.  Not just Google having on the road, but a number of companies, even your own local council  might be paying for that kind of collection.  And it costs a lot of money and often doesn't come to that area.  They don't get the imagery for years afterwards.  So the second aspect that I mentioned was, I think, an advantage of street-level imagery,  particularly on the Mapillary platform, is computer vision.  And so what you see here is data that we've collected and then processed.  And so every image you upload to our platform is being processed.  We have semantic segmentation taking place.  So this is where we identify at a pixel level what that pixel is.  So in this case, cars.  You might have seen the lanes detected.  And this is a drive down, I think, at Arts in Austria, where our computer vision team  is based.  And as far as map data, so lanes are obviously very important for anyone who's working with  map data related to roads.  There's traffic signs, which give us speed limits, location by planes, things like that.  And then underlying all this is a 3D point cloud.  So we're identifying points that we know are the same across different images, reconstructing  that.  And that allows us to know where your photo was taken a bit better than we otherwise would,  particularly if the GPS is bad.  But also to identify objects like this.  So there's trash cans.  We've got utility poles.  All things that a government or a citizen might be concerned about and want to add to  the map.  So at Mapley, we're not adding map data to OpenStreetMap.  We're not a mapping company per se, but we create this data and we make it available.  And all of this data is free and open for OpenStreetMap.  So this was what you're looking at before.  No one got the islands yet, so just keep that in mind for those that didn't see it.  It's an island somewhere in New Zealand.  But I want to talk about a history with OpenStreetMap.  We as a company were founded in October 2013.  And then since then, we kind of got involved with OpenStreetMap in the very early days.  So the first interaction was my colleague Peter, this crazy, awesome German guy who started  hacking on ID Editor, which is one of the editing tools for OpenStreetMap.  He put Mapley imagery into that tool.  And then shortly after that presented the first, or at his first added map in Argentina.  And that was where the critical momentum grew.  All these people who are editing the map and needed some sort of visual reference point  could now use Mapley to do that.  And since then, we've had integrations in Josson, which is another tool to edit the map.  Traffic signs were added.  So all this computer vision work is actually available now in OpenStreetMap.  And then the most recent thing is up in the top right is we're seeing these awesome tools  built by other people that utilize our API.  And these tools, a lot of OpenStreetMap tools now are more tasking orientated.  So you have companies, but also individuals coming in.  And rather than like this general editor that has specific tools to address problems like  roads that might be missing.  Or actually, I'll give you a few examples now.  So the first one is pick through review.  And this was the kind of the brainchild of Adrian Pabdi, a guy in France.  And he mentioned it to me in Japan in August 2017.  And it was one of those things at these conferences.  You have a lot of like great talks, you get fired up.  But a lot of the time, those talks don't really turn into anything.  So he said to me, Ed, I'd love to incorporate imagery into this tool I'm building.  And I thought, OK, that's awesome.  I hear that a lot.  But the great thing about Adrian, within a few months, he'd actually made the first  version of this.  So straight to the action.  And now there's this tasking manager, which essentially anyone can create a task.  And you can say, look, I've got images in these areas.  And I want you to help me identify which crosswalks are around here.  And how suitable are they for wheelchairs to get up and down off the sidewalk?  This one here is the number of lanes.  So you can quickly get images and then tell them or tell OpenStreetMap how many lanes are  there.  And rather than having to go into Idea Editor or Jocelyn and edit them up, it's a really  easy tool that anyone can use.  It's just a visual reference using Mapillary imagery.  The other thing that might be of interest to all of you is a plug-in.  Again, we had nothing to do with this.  This was developed by a guy called Enrico in Italy.  And this is a QGIS plug-in that allows you to see imagery alongside your existing QGIS  feature set.  So it's kind of work in progress at the moment.  But it's the first chance for you to view Mapillary imagery in QGIS.  And if you're an ArcGIS user, you can do that too with our own plugins.  So what are some of the challenges in the region?  The first one I want to share with you is in Laos.  This is, I think, a pretty cool project that the World Bank has been organizing.  So Laos, even if you look at this map, it's an incredibly difficult country to govern  because it's landlocked.  And running through it is the Mekong River.  It's got mountains.  And a lot of effects from climate change.  So roads can get washed away.  Trees can fall over on the road.  And when those roads are remote, that's really hard to maintain.  So the World Bank, supporting Laos in investing funds in the country, which is one of the  fastest growing economies in Asia, is looking at how can we maintain roads better.  And so you can see here, like, this is Mapillary coverage that they've started to get.  And they're looking at, like, working out what the quality of the road is now and then  keeping regular survey of that.  So I think they've got about 2,000 kilometers of roads that they maintain now on a regular  basis, and then it might be another 1,000 that they do on a more intermittent level.  But they use imagery to kind of get a sense of how the road is over a month to month basis  and start to build an idea of, like, what are the hotspots which need assistance more  often.  And one of the tools they're using is Complete Map.  And again, this is something anyone can create.  And it allows you to track how much imagery is in an area.  So it works out, it uses OpenStreetMap to calculate how many roads are in that area.  And then we'll tell you what percentage of those roads you've covered in a color coded  way.  So the government employees are using that.  And the last point here, this is really cool.  This is tagging.  So anyone can go in and tag objects.  So this is kind of, I guess, what we call dumb tagging.  It's just, like, literally an image and then some text associated with this particular  point here, which is the pot hole.  But what we're also doing is trying to do smarter stuff with tagging.  So we've got, like, a verification tool whereby anyone can tell us whether or not we've correctly  identified an object.  And that feeds back into our machine learning algorithms that I showed before so that we  get better and better at traffic sign recall.  So there's also tagging that's going to come on later where you can actually annotate objects  yourself.  And that'll be a real, I guess, shift, or greatly increase the number of objects we  can identify.  The other example from the Asia Pacific region, this is Chiang Mai.  So many of you might have been there.  It's a very popular expat city.  But the map is quite blank.  There's not enough, as there should be, features, not like some other cities around the world.  So one guy, he's been capturing pretty much the whole city on his motorbike.  He's got a little scooter.  He's also got a car.  His name's Johnny Carlson.  And so what he does, he goes on family road trips.  And then he'll be coming back from the family road trip and he'll say, honey, is it all  right if I take a left here?  This street isn't covered yet.  Just like, all right, Johnny.  That's all right.  We can do that.  Next trip, can I take a right here?  And the trip started getting longer and longer.  The detours before they got back home.  So I heard from someone who visited Johnny that his wife was getting increasingly annoyed  about this.  I think they're still together.  So that's all right.  But we have a lot of coverage in Thailand now.  Thanks to him.  And what are we doing with, or what's he doing with this coverage?  He's adding points of interest to the map.  So things like the shot fronts you identify here.  I mean, I wouldn't be able to add to that, but anyone that speaks Thai would be able to  get a wealth of information from that.  And that translates to POIs, so points of interest like restaurants and cafes.  So I mentioned kind of like World Bank.  I've mentioned an individual contributing.  And then there's also what we're kind of working on now.  So this is my colleague, Ryan, coordinating, capturing us.  And when a government wants capture and wants to do it more cheaply and more quickly, Ryan  has kind of put together a method to do this.  So this is Portland in Oregon.  And so in 10 days, the city of Hillsborough had their entire municipality mapped.  And then within the month, all of Portland was mapped.  And that was just with drivers who like Uber drivers or Diddy drivers, or they're just  like that flexible income that they get from being able to work when they want, in this  case, as long as it's daylight.  And they get equipped with four cameras.  And these cameras capture high resolution imagery, which is exactly what the government  needed in this particular use case.  And this use case was identifying traffic signs.  They had their existing database of traffic signs.  And they wanted to see, what are we missing?  And so when Mathillary identified a sign that they didn't have, they could put that in their  database.  This was, anyone remember?  Yeah, that's right.  Four minutes left.  OK, I want to announce something.  Camera Grant Program.  So if you're thinking about capturing from Mathillary, maybe you don't have the camera  you need.  We're trying to adjust that with a camera grant program.  So if you qualify for a camera grant, we'll send you out a camera to yours to keep.  And we're kind of sending out three types of camera that we've identified and useful  for the majority of scenarios.  So one is a dash cam that you see here, a high quality dash cam.  You just put it in your window, kind of forget about it.  Make out the SD card and upload when you're ready to do that.  The second is a GoPro, the new GoPro.  And then we have that with a setup for cars and then a setup for bikes.  So if you're on a motorbike or a cyclist, you can get that setup.  If you drive a lot, you can get the car kit.  It began as a humanitarian initiative.  So we had a lot of people in Tanzania and other African countries who needed a camera.  But then we realized there were people around the world who needed cameras to capture imagery.  So that's why we've got the program.  And I encourage you to apply it when it opens up, I think probably the end of this week.  Addressing three scenarios, making the quality of imagery better, increasing the amount of  imagery you can capture, and also allowing you to go to places with a camera.  So if you're riding a bike, it might be hard with your smartphone.  Now you have a GoPro to do it with.  So I was lucky to capture New Zealand a few weeks ago.  Very easy to do, chuck this magnetic mount on the rental car, damage the bonnet a bit,  but luckily they didn't catch that.  But I can keep a charge as I go, which is excellent.  And I captured a lot of roads in beautiful New Zealand, need an excuse to go back.  So as we depart, I encourage you to be like Adrian, that French guy I mentioned, who turns  the torque here into action.  I encourage you to drive off into the sunset with the GoPro on the front, maybe even the  back, if you're as keen as we are, and capture a lot of imagery, but don't do it at night,  because we hate that.  And last but not least, actually I'll skip this one.  Where is this?  Tasmania.  Yeah, how about Kassie?  Yes.  This is by one of our top contributors in Tasmania who I hope to meet.  Is he in the audience?  Yes, he's up the back.  Excellent.  And this is Tas Tracks as he lives and breathes.  He's captured a lot of imagery in Tasmania.  So I'll leave it with that.
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