DigitalGlobe and Open Source
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Bullock, Kevin
Adding some important and pertinent information to this abstract with respect to the recent tragic events in Nepal. DigitalGlobe, in responding to the devastating earthquake in Nepal, has openly licensed both pre-event and post-event imagery, as well as openly licensed the results of our Tomnod campaign, which has crowdsourced information from nearly 50,000 volunteer contributors to assess damage and displaced people in Nepal. DigitalGlobe is working with first responders, aid relief and NGOs including Kathmandu Living Labs, Humanitarian OpenStreetMap Team, the UN, IFRC and American Red Cross. We are providing important information to disperse relief to the growing number of displaced people. The work we have done has been featured by CNN, CCTV, Mashable, the Atlantic and many more. Please reference links below. This is a poignant example of how Geospatial data, provided in the open can benefit millions of people who need help.-intv-lake-bullock-nepal-digitalglobe.cnn-mapmakers-helping-nepal/392228/-amateur-mappers-are-helping-recovery-efforts-in-nepal/391703/-nepal-photos-before-after/#:eyJzIjoidCIsImkiOiJfdTVjdzgyb2M5aGRnMnZ1bCJ9 Original abstract: DigitalGlobe operates a constellation of high resolution, high accuracy satellites. Imagery from DigitalGlobe can be seen in Mapbox Satellite, CartoDB, Google Maps, HERE Maps, Bing Maps, Apple Maps and is often used for the purposes of contributing, editing and validating for OpenStreetMap. Over the years, DigitalGlobe has provided both imagery and software processing tools with an Open Source license. This includes post-event imagery for Typhoon Haiyan in the Philippines and the Japanese Tsunami. Recently, we open sourced a software toolkit called "Mr Geo"-open-sources-geospatial-analysis-tool.aspx This presentation will give an overview of DigitalGlobe, our geospatial technology and our services we are providing to the Open Source community.
 I'm happy to share the slides if anyone needs to see the full effect. So my name is Kevin from Digital Globe. I'm on our product team. And today I'll try to make up the time we just lost, but I'm here to just explain some of the things we're doing in open source and open data. So I'll jump around between actually vector and raster stuff we're doing. It's actually nice that the organizers scheduled me after Mark because we're doing a lot of the things I'll show today on Amazon. This is me on Twitter and GitHub if you want to get a hold of me. So I'm going to start with Hoot Nanny. Has anyone heard of this? Or, okay, Andrew, Mark. Does anyone know what this actually means, the English definition of Hoot Nanny? Does it improvise your skin? Ah, pretty good. So yeah, can't see the full thing, but it's a social gathering featuring folk singing. So actually pretty interesting. We decided to name a GitHub project after it for whatever reason. I'm not sure it's the best name in terms of marketing, but people remember it. So it actually works pretty well. So if you go to GitHub, we actually did this in collaboration with the National Geospatial Agency in the US, NGA. So the code is completely open source. It was written by DigitalGlip developers. And what it is, is it's a compilation software. So can't see the whole thing here, sorry. But essentially, this is a vector-based software set where one can take multiple vector maps and the software will actually interpret them and conflate them together and produce a third map, which is a combination of the two. And the idea here is to pick the truth or pick the best. And it's both attribute-based and geometry-based. So as you know, you can address anything multiple different ways, and often people have 10 different ways of addressing at a certain point. This software will actually interpret that and try to pick the best solution. Most of it's automated, but if you want to run it fully automated, you can. So we introduced a sort of a manual checkpoint so you can actually go in and verify things as well. So I'll just show you a couple screenshots of this. This is all based on the ID editor software. So I'll just minimize this. And you'll notice that the look and feel is a lot like ID. And this is an example of two different sources. The blue is what we're calling our source data set, and it's OpenStreetMap. And the red are a set of factors that somebody has either traced or brought in themselves. And we're going to try to conflate them together. So the algorithm will run. It will give you certain checkpoints like this and say, well, what do you think of this line? Is this right? And you can bring in Raster Image if you want to verify it. You can actually edit the line if you need to move it in a certain way, save your data. And at the end of the day or at the end of the run, it will produce a single vector set, a single map, so to speak. So I wanted to, first of all, make sure you were all aware of this. We're starting to do a lot of these projects and make the code available open source. And it goes to show people who know Digital Globe think of us as a satellite company or a source company, I think you said. That's fine, but we're doing a lot more than just that. So I'm actually going to give you a few more examples. Many of you are probably familiar or have seen our data through OpenStreetMap. So if you're an in-ID editor, you're doing your work, you're pulling an image. In-ID editor or JAWSM, you can pull in Bing imagery or MapQuest imagery, MapBox. Most of that actually comes from Digital Globe. So we license our imagery to Bing. They make it available. Bing cooperation with us to OpenStreetMap. So if you've used OpenStreetMap, you've probably used Digital Globe imagery. And that's great. We're very happy with all the work that has taken place at OpenStreetMap and more excited about the future. I wanted to give one example that was really powerful this year in terms of our work in the open source community. And that was the earthquake in Nepal. This big square represents about a half million square kilometer area over Nepal. So you can – Nepal's in here in the middle. This was all the imagery we collected after the earthquake. So it was a massive amount of data. Unlike Landsat and Nape, when you acquire things at 30 centimeter or 50 centimeter resolution, the data is huge or the data are huge if I'm being precise, right, Mark? So we're talking terabytes of data. Our entire archive is close to 100 petabytes of data. Our biggest problem right now is we have too much data. So that's where Amazon S3 actually comes in and helps us out a lot. So after Nepal, we started collecting imagery the day after. Well, so the earthquake happened on Saturday. This was the image from Sunday. It was cloudy and rainy, but for whatever reason, a hole opened up in the clouds and we were able to point our satellite through that hole. And Kathmandu is actually right here. What's interesting is we design our satellites. We're not a microsat company. We're a macro-sat company. I don't even know if that's a term. But our satellites are big because they have massive telescopes. And it's really just photography 101. We need a big focal length to get a high-resolution pixel. But also our satellites can maneuver. So what we were doing while the weather was bad in Nepal, our satellites would pass over. And some of the images we'd actually be over the Indian Ocean or over Bangladesh pointed back looking at Nepal. It actually worked beautifully. In fact, it was very satisfying. All that work we put years and years into building these satellites. It actually worked. It actually saved lives. It actually made a difference. But the way we made a difference is by releasing it open source and involving a big crowd of people. So, HOT OSM, there's about 6,000 volunteers. We launched, we have a crowdsourcing platform called Tomnod. We launched it. We had 60,000 volunteers. Tomnod, the way I describe it, it's like, it's a crowdsourcing platform for anyone. My 7-year-old daughter can do it, my 90-year-old grandmother can do it. They can't do open street map editing, but they can do Tomnod because it's very binary. It's like, do you see a tent? Yes or no? So, we launched a big campaign. Had 60,000 volunteers, millions of data points. We released that as open source as well. And so, people smarter than I, this is a map made by Bill Morris. He actually took the open street map data, the Tomnod data, put them together, and it helped inform the work we were doing after the earthquake. What was really interesting to me in this scenario is we weren't looking for toppled buildings. Everyone knew where the toppled buildings were. We were looking for displaced people, internally displaced people. So we're looking for these camps that were building up after the crises to make sure people who were displaced had food and water and medicine. So it wasn't really the earthquake. It was the response after the earthquake. Nepal's in the past. The important thing is we're building an infrastructure and a community for what happens in the future. There's going to be another event somewhere in the world where people need help. And what drives me and makes me happy going to work every day is that we can actually, sitting in Colorado, we can actually provide data and information around the world to people who need it most. And we do that through open source. Recently we launched a developer portal. You can go to developer.digitalglobe.com and you'll see a couple APIs there. One is a Maps API. So when Mark showed the Slippy Map, that's exactly what this is. It's built on the Mapbox API, but it allows you to access a global 30 centimeter color balance mosaic of the world, which again is a hard thing to do. But it's all based on Amazon S3. You can sign up for an evaluation. If you want to actually subscribe, we just charge by the CPM by transaction. So we have a global map of the world available there. We also have something down here, it's a little bit cut off, but we call it geospatial big data. So this is the problem of having too much data. We don't want to ship. If someone were to say, I need to solve some sort of global problem, we don't ship them 100 petabytes of data. We say go to the cloud, hit an API, and bring your algorithm. I think Mark said, don't copy the data, but bring your workflow into the cloud. That's exactly what we're doing. So through geospatial big data, you can see every image we've ever collected. That goes back to 1999. All around the world, you have access to it all in the cloud. There's no such thing as delivering data through this platform. You can view it, you can analyze it. We have a bunch of algorithms, you can run on it, or if you want to bring your own algorithms, you can as well. So really interesting use cases are being developed out of that. So I just want to share a few, and then Mark, I don't know how I'm doing on time, but I'll share a few and then open it up for questions. About 10 minutes. Okay. The principles behind geospatial big data is everything happens at a certain location at a certain time, and there's a bunch of context around that that we need to understand. And this can be past or future. And so, like, here's a beautiful image of a river, but what's really important is the interpretation of that and actually building out a land use map. So you can see the legend here of what's actually in that image. I see imagery almost as a novelty, almost like, yeah, it's great to look at. What's really important are the things within that image. That's what makes us, or allows us to make better decisions. Just a few more. This is actually Tokyo, Japan. An image with a ton of stuff in it. We can actually start running interpretation models and find individual buildings. So we're able to take an imagery mosaic of all of Japan, run a model on it, and find every single building, every single structure within hours using this platform. Again, here, image. This is somewhere in Africa. Actually, this is in Somalia. When you look at this image, again, it's interesting, but what is it telling me? If you run some analysis on it, we can actually find population density. So this is a population density map of that image. So that's actually what's important if you're a humanitarian agency or a government. Again, it's amazing we're spoiled in places like Korea and the United States. We have a robust government infrastructure. We have a census. Most of the world doesn't have a census. So geospatial data is a great way of creating a census. So once you know population density, these are the village boundaries. These are the actual homes and structures within that. And then scale it out to the Horn of Africa. So we can find everywhere where people live, quantify it, and there's many reasons why we're doing that. Recently we were able to find a slave ship in Papua New Guinea. This made the news a couple of weeks ago. We were working with an investigative reporter, and we found this slave ship from space. And it turns out this is a commercial fishing boat, and these two boats right here are slave boats. And what happens is they, it's remarkable that this happens, but there are hundreds of thousands of people that are enslaved today. What happens, these are Burmese men who are lured with the hopes of having a good job, and then they're put to work on a slave ship, work 22-hour days, barely get enough food to survive. They were stuck out at sea for a couple of years. We found them, and so eight men were actually liberated because of this. But it's just amazing, like these are the types of things that we're able to do using cool satellites and then geospatial analytics, and a crowd of people to help us out. The last one, this one's, if you've turned on the news, you've seen the refugee crisis. So this was a really hard one to solve, and we're actually still trying to figure out how to solve it. And one of the reasons I came to this conference was not to show you how smart and cool we are, but it's really to enlist help and thoughts. So I'm here more to learn than I am to present anything. But right now, you have hundreds of thousands of refugees from Syria and Afghanistan trying to find safe haven. So this is a border in Hungary from a year ago, and we were able to look at it just from a few days ago. And they've actually created a razor wire fence. You can actually see the difference in the two, and this is the border. So they created razor wire fence to prevent migrants, which is a whole other story I won't get into. But the point here is we're able to show that and show what's going on. And actually in this image, there's a border. There's a truck that I think it's like a border, you know, some sort of border agent going up into the border. And you can actually see a group of people. Those are individual people there who are trying to get through this fence. Now I'm not sure what to do with this information, but we're collecting it, and we want to make it available, and hopefully all of us can make sense of it. I think that's the biggest point here is we have tons of data. We have NAIP data, we have Landsat, we have Digital Globe data, we have Earthcast data is coming online. How do we get together and make sense of it all and make good decisions? So that's hopefully a little bit of like inspiration to end my talk, but that's all I had today. So thank you, and I'm happy to take questions in the last whatever minutes we have. Thank you. We have five minutes. Yeah. You could add a few vector sources. Yeah. And to validate it. And to validate it, you could use raster data. Yeah. Do you do that in an automated way, or is it a manual verification? So we have a way of doing it in an automated way. It's essentially an algorithm that looks for linear paths or roads and then can pull a vector to align with that road. It's not always the best way of doing it. With Hoot and Annie, what we see most are editors who they're the experts of the particular area actually doing manual editing themselves. And that's kind of like that human curation of the data set, which is probably always going to be better than automated. So it's a really good question. We can actually do them both, and going back to that geospatial big data platform, we're actually going to put both those algorithms on the platform. So you can pull down an image. The algorithm we have, it's called Road Tracker. Select Road Tracker and essentially extract every road within an image. And then you can conflate it, you can edit it, or whatever. Yeah. But again, great question. And that's like, to me, that's the exciting thing of how are we going to pull features and information out of an image? So if there's no more questions, I'm around all week. Please hit our developer portal. You can ask us questions, ask me questions. But please provide feedback. And that's really one of the main reasons I came here. So thank you. Oh, another question. Yeah, I don't know that much about the kind of satellites in Madrid, but I know that there's been a new satellite that's been launched recently by the European Space Agency and they're putting another one up next year. Yeah. I've read something that they were going to open source all the data and it'd be like a five-day refresh. Is that like the Sentinel? Yeah, I think it's Sentinel one. Yeah. So do you do anything with data from, you know, if that data becomes open and public domain, you do anything with that? Yeah. Good question. So I think the Sentinel would be comparable to our Landsat. Someone can fact check me on that, but I think Sentinel is kind of the European version of a Landsat program. The answer is within our platform, we actually pulled in all of the Landsat data set. We pulled in SRTM, which is the, it's from NASA, it's the Shuttle Radar Topography Mission. It's basically an elevation model of the entire world. So the answer is when data becomes open source, we do pull it into our platform. So you're not only looking at imagery from our satellites, but from other satellites as well. So the answer is let's figure out what satellite that is and our platforms are data agnostic. So it doesn't matter if they come from a UAV or a Sentinel satellite, we can deal with it. But actually, our philosophy is it's better to aggregate as much sources as possible because no one source could ever be the truth. So we pull in multiple sources and that tends to work better for our users. Okay. Thanks. Thank you. That's a good question. All right. 1148. So you have two minutes to make the, I don't think there's another talk in here. So you have two minutes to go make your next session and thank you for your time today. Thanks for coming. Thanks. Thank you.
 I'm happy to share the slides if anyone needs to see the full effect.  So my name is Kevin from Digital Globe.  I'm on our product team.  And today I'll try to make up the time we just lost, but I'm here to just explain some  of the things we're doing in open source and open data.  So I'll jump around between actually vector and raster stuff we're doing.  It's actually nice that the organizers scheduled me after Mark because we're doing a lot of  the things I'll show today on Amazon.  This is me on Twitter and GitHub if you want to get a hold of me.  So I'm going to start with Hoot Nanny.  Has anyone heard of this?  Or, okay, Andrew, Mark.  Does anyone know what this actually means, the English definition of Hoot Nanny?  Does it improvise your skin?  Ah, pretty good.  So yeah, can't see the full thing, but it's a social gathering featuring folk singing.  So actually pretty interesting.  We decided to name a GitHub project after it for whatever reason.  I'm not sure it's the best name in terms of marketing, but people remember it.  So it actually works pretty well.  So if you go to GitHub, we actually did this in collaboration with the National Geospatial  Agency in the US, NGA.  So the code is completely open source.  It was written by DigitalGlip developers.  And what it is, is it's a compilation software.  So can't see the whole thing here, sorry.  But essentially, this is a vector-based software set where one can take multiple vector maps  and the software will actually interpret them and conflate them together and produce a third  map, which is a combination of the two.  And the idea here is to pick the truth or pick the best.  And it's both attribute-based and geometry-based.  So as you know, you can address anything multiple different ways, and often people have 10 different  ways of addressing at a certain point.  This software will actually interpret that and try to pick the best solution.  Most of it's automated, but if you want to run it fully automated, you can.  So we introduced a sort of a manual checkpoint so you can actually go in and verify things  as well.  So I'll just show you a couple screenshots of this.  This is all based on the ID editor software.  So I'll just minimize this.  And you'll notice that the look and feel is a lot like ID.  And this is an example of two different sources.  The blue is what we're calling our source data set, and it's OpenStreetMap.  And the red are a set of factors that somebody has either traced or brought in themselves.  And we're going to try to conflate them together.  So the algorithm will run.  It will give you certain checkpoints like this and say, well, what do you think of this  line?  Is this right?  And you can bring in Raster Image if you want to verify it.  You can actually edit the line if you need to move it in a certain way, save your data.  And at the end of the day or at the end of the run, it will produce a single vector set,  a single map, so to speak.  So I wanted to, first of all, make sure you were all aware of this.  We're starting to do a lot of these projects and make the code available open source.  And it goes to show people who know Digital Globe think of us as a satellite company or  a source company, I think you said.  That's fine, but we're doing a lot more than just that.  So I'm actually going to give you a few more examples.  Many of you are probably familiar or have seen our data through OpenStreetMap.  So if you're an in-ID editor, you're doing your work, you're pulling an image.  In-ID editor or JAWSM, you can pull in Bing imagery or MapQuest imagery, MapBox.  Most of that actually comes from Digital Globe.  So we license our imagery to Bing.  They make it available.  Bing cooperation with us to OpenStreetMap.  So if you've used OpenStreetMap, you've probably used Digital Globe imagery.  And that's great.  We're very happy with all the work that has taken place at OpenStreetMap and more excited  about the future.  I wanted to give one example that was really powerful this year in terms of our work in  the open source community.  And that was the earthquake in Nepal.  This big square represents about a half million square kilometer area over Nepal.  So you can – Nepal's in here in the middle.  This was all the imagery we collected after the earthquake.  So it was a massive amount of data.  Unlike Landsat and Nape, when you acquire things at 30 centimeter or 50 centimeter resolution,  the data is huge or the data are huge if I'm being precise, right, Mark?  So we're talking terabytes of data.  Our entire archive is close to 100 petabytes of data.  Our biggest problem right now is we have too much data.  So that's where Amazon S3 actually comes in and helps us out a lot.  So after Nepal, we started collecting imagery the day after.  Well, so the earthquake happened on Saturday.  This was the image from Sunday.  It was cloudy and rainy, but for whatever reason, a hole opened up in the clouds and  we were able to point our satellite through that hole.  And Kathmandu is actually right here.  What's interesting is we design our satellites.  We're not a microsat company.  We're a macro-sat company.  I don't even know if that's a term.  But our satellites are big because they have massive telescopes.  And it's really just photography 101.  We need a big focal length to get a high-resolution pixel.  But also our satellites can maneuver.  So what we were doing while the weather was bad in Nepal, our satellites would pass over.  And some of the images we'd actually be over the Indian Ocean or over Bangladesh pointed  back looking at Nepal.  It actually worked beautifully.  In fact, it was very satisfying.  All that work we put years and years into building these satellites.  It actually worked.  It actually saved lives.  It actually made a difference.  But the way we made a difference is by releasing it open source and involving a big crowd of  people.  So, HOT OSM, there's about 6,000 volunteers.  We launched, we have a crowdsourcing platform called Tomnod.  We launched it.  We had 60,000 volunteers.  Tomnod, the way I describe it, it's like, it's a crowdsourcing platform for anyone.  My 7-year-old daughter can do it, my 90-year-old grandmother can do it.  They can't do open street map editing, but they can do Tomnod because it's very binary.  It's like, do you see a tent?  Yes or no?  So, we launched a big campaign.  Had 60,000 volunteers, millions of data points.  We released that as open source as well.  And so, people smarter than I, this is a map made by Bill Morris.  He actually took the open street map data, the Tomnod data, put them together, and it  helped inform the work we were doing after the earthquake.  What was really interesting to me in this scenario is we weren't looking for toppled  buildings.  Everyone knew where the toppled buildings were.  We were looking for displaced people, internally displaced people.  So we're looking for these camps that were building up after the crises to make sure  people who were displaced had food and water and medicine.  So it wasn't really the earthquake.  It was the response after the earthquake.  Nepal's in the past.  The important thing is we're building an infrastructure and a community for what happens in the future.  There's going to be another event somewhere in the world where people need help.  And what drives me and makes me happy going to work every day is that we can actually,  sitting in Colorado, we can actually provide data and information around the world to people  who need it most.  And we do that through open source.  Recently we launched a developer portal.  You can go to developer.digitalglobe.com and you'll see a couple APIs there.  One is a Maps API.  So when Mark showed the Slippy Map, that's exactly what this is.  It's built on the Mapbox API, but it allows you to access a global 30 centimeter color  balance mosaic of the world, which again is a hard thing to do.  But it's all based on Amazon S3.  You can sign up for an evaluation.  If you want to actually subscribe, we just charge by the CPM by transaction.  So we have a global map of the world available there.  We also have something down here, it's a little bit cut off, but we call it geospatial big  data.  So this is the problem of having too much data.  We don't want to ship.  If someone were to say, I need to solve some sort of global problem, we don't ship them  100 petabytes of data.  We say go to the cloud, hit an API, and bring your algorithm.  I think Mark said, don't copy the data, but bring your workflow into the cloud.  That's exactly what we're doing.  So through geospatial big data, you can see every image we've ever collected.  That goes back to 1999.  All around the world, you have access to it all in the cloud.  There's no such thing as delivering data through this platform.  You can view it, you can analyze it.  We have a bunch of algorithms, you can run on it, or if you want to bring your own algorithms,  you can as well.  So really interesting use cases are being developed out of that.  So I just want to share a few, and then Mark, I don't know how I'm doing on time, but I'll  share a few and then open it up for questions.  About 10 minutes.  Okay.  The principles behind geospatial big data is everything happens at a certain location  at a certain time, and there's a bunch of context around that that we need to understand.  And this can be past or future.  And so, like, here's a beautiful image of a river, but what's really important is the  interpretation of that and actually building out a land use map.  So you can see the legend here of what's actually in that image.  I see imagery almost as a novelty, almost like, yeah, it's great to look at.  What's really important are the things within that image.  That's what makes us, or allows us to make better decisions.  Just a few more.  This is actually Tokyo, Japan.  An image with a ton of stuff in it.  We can actually start running interpretation models and find individual buildings.  So we're able to take an imagery mosaic of all of Japan, run a model on it, and find  every single building, every single structure within hours using this platform.  Again, here, image.  This is somewhere in Africa.  Actually, this is in Somalia.  When you look at this image, again, it's interesting, but what is it telling me?  If you run some analysis on it, we can actually find population density.  So this is a population density map of that image.  So that's actually what's important if you're a humanitarian agency or a government.  Again, it's amazing we're spoiled in places like Korea and the United States.  We have a robust government infrastructure.  We have a census.  Most of the world doesn't have a census.  So geospatial data is a great way of creating a census.  So once you know population density, these are the village boundaries.  These are the actual homes and structures within that.  And then scale it out to the Horn of Africa.  So we can find everywhere where people live, quantify it, and there's many reasons why  we're doing that.  Recently we were able to find a slave ship in Papua New Guinea.  This made the news a couple of weeks ago.  We were working with an investigative reporter, and we found this slave ship from space.  And it turns out this is a commercial fishing boat, and these two boats right here are slave  boats.  And what happens is they, it's remarkable that this happens, but there are hundreds  of thousands of people that are enslaved today.  What happens, these are Burmese men who are lured with the hopes of having a good job,  and then they're put to work on a slave ship, work 22-hour days, barely get enough food  to survive.  They were stuck out at sea for a couple of years.  We found them, and so eight men were actually liberated because of this.  But it's just amazing, like these are the types of things that we're able to do using  cool satellites and then geospatial analytics, and a crowd of people to help us out.  The last one, this one's, if you've turned on the news, you've seen the refugee crisis.  So this was a really hard one to solve, and we're actually still trying to figure out  how to solve it.  And one of the reasons I came to this conference was not to show you how smart and cool we  are, but it's really to enlist help and thoughts.  So I'm here more to learn than I am to present anything.  But right now, you have hundreds of thousands of refugees from Syria and Afghanistan trying  to find safe haven.  So this is a border in Hungary from a year ago, and we were able to look at it just from  a few days ago.  And they've actually created a razor wire fence.  You can actually see the difference in the two, and this is the border.  So they created razor wire fence to prevent migrants, which is a whole other story I won't  get into.  But the point here is we're able to show that and show what's going on.  And actually in this image, there's a border.  There's a truck that I think it's like a border, you know, some sort of border agent going  up into the border.  And you can actually see a group of people.  Those are individual people there who are trying to get through this fence.  Now I'm not sure what to do with this information, but we're collecting it, and we want to make  it available, and hopefully all of us can make sense of it.  I think that's the biggest point here is we have tons of data.  We have NAIP data, we have Landsat, we have Digital Globe data, we have Earthcast data  is coming online.  How do we get together and make sense of it all and make good decisions?  So that's hopefully a little bit of like inspiration to end my talk, but that's all I had today.  So thank you, and I'm happy to take questions in the last whatever minutes we have.  Thank you.  We have five minutes.  Yeah.  You could add a few vector sources.  Yeah.  And to validate it.  And to validate it, you could use raster data.  Yeah.  Do you do that in an automated way, or is it a manual verification?  So we have a way of doing it in an automated way.  It's essentially an algorithm that looks for linear paths or roads and then can pull a vector  to align with that road.  It's not always the best way of doing it.  With Hoot and Annie, what we see most are editors who they're the experts of the particular  area actually doing manual editing themselves.  And that's kind of like that human curation of the data set, which is probably always  going to be better than automated.  So it's a really good question.  We can actually do them both, and going back to that geospatial big data platform, we're  actually going to put both those algorithms on the platform.  So you can pull down an image.  The algorithm we have, it's called Road Tracker.  Select Road Tracker and essentially extract every road within an image.  And then you can conflate it, you can edit it, or whatever.  Yeah.  But again, great question.  And that's like, to me, that's the exciting thing of how are we going to pull features  and information out of an image?  So if there's no more questions, I'm around all week.  Please hit our developer portal.  You can ask us questions, ask me questions.  But please provide feedback.  And that's really one of the main reasons I came here.  So thank you.  Oh, another question.  Yeah, I don't know that much about the kind of satellites in Madrid, but I know that there's  been a new satellite that's been launched recently by the European Space Agency and  they're putting another one up next year.  Yeah.  I've read something that they were going to open source all the data and it'd be like  a five-day refresh.  Is that like the Sentinel?  Yeah, I think it's Sentinel one.  Yeah.  So do you do anything with data from, you know, if that data becomes open and public domain,  you do anything with that?  Yeah.  Good question.  So I think the Sentinel would be comparable to our Landsat.  Someone can fact check me on that, but I think Sentinel is kind of the European version  of a Landsat program.  The answer is within our platform, we actually pulled in all of the Landsat data set.  We pulled in SRTM, which is the, it's from NASA, it's the Shuttle Radar Topography Mission.  It's basically an elevation model of the entire world.  So the answer is when data becomes open source, we do pull it into our platform.  So you're not only looking at imagery from our satellites, but from other satellites  as well.  So the answer is let's figure out what satellite that is and our platforms are data agnostic.  So it doesn't matter if they come from a UAV or a Sentinel satellite, we can deal with  it.  But actually, our philosophy is it's better to aggregate as much sources as possible because  no one source could ever be the truth.  So we pull in multiple sources and that tends to work better for our users.  Okay.  Thanks.  Thank you.  That's a good question.  All right.  1148.  So you have two minutes to make the, I don't think there's another talk in here.  So you have two minutes to go make your next session and thank you for your time today.  Thanks for coming.  Thanks.  Thank you.
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