The Borked Supply Chain. How the Telekom brings FOSS Projects into Stable Production
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Christl, Arnulf
This talk focuses on aspects of transitioning Open Source software projects into productive environments. The Deutsche Telekom AG has set out to use FOSS software to build a comprehensive geospatial data management and processing environment based on cloud technology. Some components (like PostGIS and QGIS) are used as COTS (commercial off the shelf) products. Others (like GRASS GIS) are used as libraries to implement intricate parts of an incredibly specific process to dig optimized trenches for fiber optics cables throughout Germany. The project uses agile methods to implement this architecture with FOSS products and projects and hand crafted implementations to achieve it's objectives. If we use the analogy of a bridge across a deep valley to achieve the objectives, then it feels like going full speed on a downhill bike, jumping into thin air and reaching the other side of the valley in a truck carrying internet access for millions landing on a concrete bridge that has manifested halfway through. A bit frightening, but so cool! That's FOSS! Before going into implementation details we have to clarify some terminology: In the physical world, a project is planned for a specified time with a specified budget and clearly defined objectives. Think: building a bridge. It will be unique because it spans across a specific valley and has to account for a specific geology and specific use (people, donkeys, cars, trucks, trains or electrons). Building the bridge will require a plan, excavators, trucks, steel, concrete, asphalt, cables and so on. When the project is "done" people, goods and electrons can cross the bridge: Even donkeys can use it. Another example for the same terminology in a different context is the process of making a new car. This will start with a project focused on achieving the objective of making a new car. Once the car has become a reality, the project ends. The product itself gets reproduced (and sold) as often as possible to allow people to drive across the bridge which was the end product of another project. In the FOSS realm things are a Megabit different. A FOSS software project is an ongoing effort to create software, typically by a diverse and sustainable developer community. Some projects have been around for decades (think GRASS GIS). They are never "done". Others stick around for a few years while they are needed and then either get outdated or replaced by a contender (see Community MapBuilder a good decade ago). In general, when it gets started, there is no predefined end to a software project. Sometimes the founder of a software don't even know that they are founding a software (think Gary Sherman writing the first lines of what today is better known as QGIS). Sometimes Open Source software projects become part of a product. This typically happens in a downstream effort by a completely different set of actors (think Google's Kubernetes turning into the core of RedHat's Openshift). When it comes to combining all of these realms within a project aiming at achieving an objective by using FOSS software projects – things can really become a bit confusing. Hopefully we are going to be able to clarify a few things as we go through our presentation(s). Authors and Affiliations – Arnulf Christl, Metaspatial Senior Consultant, terrestris GmbH & Co. Kg., Germany Track – Use cases & applications Topic – Business powered by FOSS4G Level – 2 - Basic. General basic knowledge is required. Language of the Presentation – English
 Hello, so welcome to the next talk. It's a talk from Arnov Crystal and he should be here now and joining us live, but unfortunately he can't join. He's in the train. I'll show him for a minute, but the internet is not so good there. So we have his talk recorded and will broadcast it soon. And Arnov will be in the chat so you can meet him in the conference room and ask questions there. So his talk is the Borg supply change and it's about the telecom and how the telecom brings force projects into stable production. Some words about Arnov before we start. So most of you might know Arnov Crystal. He was one of the founders of OSGEO in 2006. He is an OSGEO advocate and co-founder of OSGEO. He's working at Terrestris at the moment and he is a senior consultant there for telecom AG. He is working and on the other side he has metaspatial, the platform to coordinate smart mapping and he is a scrum master also. So welcome to his talk and hope to see Arnov in the chat. Hello, my name is Arnov Crystal and I should be in Buenos Aires now, but unfortunately it's not possible. So I wanted to do this live but again this is not possible because they just cancelled my night train. So I have to take an earlier train and I'm not really sure they will work over the internet connection that we get on the train. So better record this. Sanctinia posters, Geo server, Shogun, open layers and many more goodies in the cloud. It's a talk by Arnov Crystal also known as 7. What are my roots? I am a carpenter and former director of president of OSGEO, entrepreneur and Buddhist scholar. I am Borg, so beware. My affiliations I'm working for Terrestris, GMBH and CoKG and metaspatial.com which is a consulting or agile product management. They are the Raster guys, Terrestris is the Vector guys, they do the web viewing and the operation and obviously Borg Inc. So if you have an issue with artificial intelligence please come to me and I will help. I wanted to do this live but again this is not possible because they just cancelled my night train. So I have to take an earlier train. So I summary this this year and wait for my second business like the also oflar, which My affiliations, I'm working for Terrestris, GMBH and CoKG and Metaspatial.com, which is a consulting for Agile product management in Mundialis. They are the Raster guys, Terrestris is the Director guys, they do the web viewing and Mundialis the operation and obviously Bork Inc. So if you have an issue with artificial intelligence, please come to me and I will help you. So we have nine points in an open source tool chain and we'll look at them. So first question is what do we want to do? Next, where are the pain points in the current way with doing things? And we design a new process, I will show in a prototyping demo what is going on. Then we find the right open source tools to make it better, decide on an architecture, putting the pieces together, results in a demo again and seven of nine just because I had nine points. So I thought I should add this ninth point. What do we want to do? We want to roll out fiber optics in all of Germany because Germany is too slow. We have no fiber optics due to political strangeness in the late 80s and 90s. So everything we have is copper, which is nice but slow. And therefore we really have to speed up the planning process, otherwise we're going to be lost. How to do that? What are the tasks? What do you need to do to roll out fiber optics? First you need to collect data and then you need to analyze and process the data and use the data to position the distributor boxes, which need to have a certain distance from each other, then find potential trenches where you can actually put the fiber optics and generate permit application forms because only then you can start to dig and actually put the fiber optics into the ground. And all of this planning process should be automated. Where are the current pain points? It currently is an on-site manual planning, so that means people actually have to walk to a place and take pictures of potential positions for collector boxes. And there is limited scanning options because you need so many people to drive around and do this that it's just not possible with the current people we have. There is still a lack of data because Germany is not fully open data and we have legacy systems, as you can imagine, and a big TataCom provider in the basement. There is really old stuff running there. We have waterfall project management and maybe we need a new mindset. This will be a little excursion. I'll go into the manifesto for agile software development, which they have decided is the right way to go forward. And what we do here is we put individuals over interactions, pardon me, individuals and interactions over processes and tools. And we need comprehensive documentation, but we think that working software is more important. We want customer collaboration over contract negotiation and we want to respond to change instead of strictly following a plan. That is, while there is value on the items on the right, we value the items on the left more. And as you can imagine, this really is a new mindset and for a big old company with many, many people and lots of hierarchies and bosses and stuff, it's not easy to implement. But I think we succeeded well. So let's go to the prototyping and have a look at what we have. And this will be a kind of live demo. So I'll switch to post-cubius. And we'll see what you always see when you start with GIS project in the wild. It's an open street map WMS. And just to show this is Hamburg, a few data that we use for input, we obviously need the buildings we want to put the glass fiber. And we need to have the landlord and land parcels. And then we need to know who owns which part. So this is probably public and this is probably private and all the other ones, we're not quite sure. So there's some risk in doing that. But that's all the data that German data is currently showing us. So this is part of the basic data that we need. And then we have more data, which is a lot more detailed. That's this data. And this has been collected by a surface car, which actually actually runs on the ground. And we've seen this before. And these are objects that have been detected by KNN, which is the Kunstgesnarl-Wonelles-Netzwerk, which is an artificial neural network. And this is also detected different surface types. So we have objects and we have surface types. And this is great. And all the planning areas that we are going to use have been traveled with this surface car. But it's not enough because as you can see in the back here, no car can drive there. So we have no data. So additionally, we have added orthophoto classification, which complements the KNN data here. And you can see there is some overlap in the data, but there's also some differences. So obviously, we also have orthophoto data. Let's take out the other data so that we can see it. Here we go. And if we go into a detailed situation like this, then we can see that the KNN data is quite detailed here. We can see this is asphalt and this is concrete data. And these are the, let's take out the digital orthophotos again. These are the floors. And what we need is the trenches. And the trenches have to have a certain distance from the outside of the places in connection to the, where are they? You can see this is really a slow process. If you want to do it in an animated, this one here, this is the road area. And the road area from the road area here, you have to measure. And this is going to be, where is my measure tool? I lost it. There it is. This needs to be around 40 centimeters. So from here to there, it's around 70 centimeters. And that's the distance which needs to be taken from the outside of the building. And the building is going to be here. Can put building here. So it's a conflation of building data and of cadastral data and of road data. And all of on top of this, you can now see in the orthophoto how it all comes together. Come on, orthophoto. There you are. Okay. So let's go back to the presentation. And after the prototyping, we know what we're doing. We're trying to position those collector boxes. And we're trying to find out which is the best place to put trenching. We need to design the new process. Because what we do in the trenching is we collect data and collect even more data and collect even more data and then conflate this and process this and calculate and automate everything. What do we have to calculate? We have to calculate what the cost of these different lines is going to be. So if we look at the different costs here, and you can see that depending on the surface, it's more expensive or less expensive to dig a road there, to dig a trench for a fiber cable there. So this information is basically deciding then which path you're going to take when you dig your new trenches. Okay, data, collecting data, open data in the states of Germany is a little not so nice because there are some of them is open, some of them is partly closed, some of them is really closed. And so it's a little difficult to get an overview. In places where we can't get any data or we don't get any open data, we obviously go to take some open street lab data. And where we can't get those for the processing of the surfaces, we also need orthophotos. And then we have this surface car which drives around and has cameras here for panorama pictures which we'll see later. And also a lighter scanner at the back and a high precision GPS so that we will know where we are. Okay, now we have the data and we need the tools. What tools do we need? I think this is the easy one. CuriosityChef's Zine, it's great for prototyping ETL, GDR, GDR or GR and all other tools. And yes, we have some FME in there. There is Grasse and Dictinia, there was an Dictinia workshop in the beginning of this conference. Provisioning happens with G-server and GeoNetwork and viewing and planning is done with Shogun fiber maps which is based on open layers and operating the whole thing in CI-CD with Kubernetes, monitoring Kibana, Grafana and all the other stuff you get in the cloud. So let's look at the spatial data infrastructure and there is some OSM data coming in here with the OSM importer into a post-gis database. This is like our core vector database. That's conditional server data, compared with post-nase or with FME to go into the post-gis database and is provided with GeoServer as a Geospatial service. The metadata is collected by GeoNetwork open source and metadata and later on fiber maps will use in the web client this metadata to actually access this service to get to this data. So it's all connected. If you look at it at the processing perspective you have T-surface data which comes in through the Kunstlis-Noronautis-Netzwerk which is the artificial neural network which will produce outcome data which is resulting in the classified surface cover and the same for all the photos which are calculated with GeoCutinia which leverages grass modules to actually do this and all of this if it is a vector result also ends up in the post-gis database. Next is the planning and distribution boxes and potential trenches and this happens in fiber maps which is a web client application which accesses these Geospatial services which take the data coming out of here and actually also can remote control actinia which can then pull new T-Food surface data through KNN, FME classified surface cover put in the results here so that fiber maps can continue to collect this because we don't calculate all of Germany all the time as you've seen it's very detailed data that would probably take half a year on very big machines and then the data is already outdated. So this is an on-demand processing change. Okay designing the architecture the OOTC telecom cloud was a given no Azure no AWS it's OATN telecom cloud and we had a red-edged open shift which is currently transitioning back to Kubernetes we have a GitLab and Ansible and Puppet and we provisioned the whole thing with Terraform which has been replaced by Steep recently the whole thing is deployed with Helm and if we put the things together it looks like this we have a setup in workbench in GitLab and open telecom cloud with open stack then we provisioned with Terraform lots of virtual machines in these we have red-edged open shift with master infrastructure nodes and bastion IDMSFTP proxy and fdb and duty b these are configured with Ansible and Puppet and then we deploy them with Helm so that we get Docker images and the whole thing is CICD pipeline where devs push stuff into the app repository which is compiled unit tested image build pushed image into the magenta trusted registry and on the other side the ops push the prod and staging SDI which comes from the pulled images into dev and staging a prod because deploy diff and lint so the whole thing is running with a workbench in GitLab it's really a nice setup but it took about two years and I don't know how many years of work in person days to actually get this up and running so fiber optics rollout planning on speed and this is going to be a small live demo and after that I should be so this is how it looks like when you plan these things and all of these little triangles and green dots and gray dots are collector boxes and we're going to now move these collector boxes and this is the T surface data which has been collected with lighter data and panorama images and we'll have a look at one of those panorama images here so we start the standout sicherel which is the place where you can put the collector boxes and you can see that there is some images so we want to have a look at the image in the real world so what we do is we move over to fiber d3d this is the position down there where this one is we're looking at and when we switch to fiber d3d this is a different web application it's fired up now and it loads the point cloud and panorama images and positions this object in the middle of it so this object is actually a vector data image which is projected into the panorama images at the right point this is it you can move around in the image and we can also there you can see the cone of the direction where we're looking at and if we zoom into the picture then we will also see now we're zooming into the picture that the cone is adjusting its size and direction to show where we're actually currently looking at so we always have the orientation in the map if we move to a different position we can just click there and we'll see a different view of the next road crossing that we are here and this is it and I'll stop here for a moment because now you can see the t-surface card this is the t-surface card which takes the data with the lighter scanner and the cameras taking the pictures and for a moment you will see now the point cloud there it is and then all the pictures which are stitched on top so that you have a nice 3d view now we will go back to the box that we have just looked at and we will move the box to a new position it's wrongly positioned in the garage so we will take it out and put it inside outside of the garage again there we go I'm sorry that this is a recording but I was afraid that it wouldn't work for presentations not nice so once we've positioned it there we can go to the next step and store this information if we were happy with the position now we can take pictures this is like walking there and actually taking a camera and taking a picture of the place where we want to put the box but the box is already there which is pretty cool and for the permit we need different perspectives so we move a few meters onwards and then turn around and look better at the box and take the next picture and again it's like positioning we can move it we can even zoom in or out whatever we need to get the right perspective for the permit which we will let it produce here you can now see the old and the new position and from where it comes we want to save these pictures so now we're processing the pictures and putting them back in and here they show up so this is our processing interface and now we want to make a report so we select this collector box to produce a report and this report is the PDF which gets sent to the public administration which will then evaluate whether the position of this box is allowed at the current phase where it is shown so once you get the PDF you can open it up and you see here that you have a number the postal code the name of the address and this is the box there is quite a few different sizes standard sizes of boxes that you can position and you have an overview map and this is the two pictures where you can see the before and after and before and after of this position and this is what actually is enough for the community to allow whether to put this box in the corresponding place or not so let's fast forward to another perspective this is now looking at another place in where you don't have positioning it's just you can you go any place that you've traveled it's like google street view it's not much of a difference i think the quality is much more detailed obviously so then let's have a look at the planning process itself so this is the planning process and if we go further zoom further in and we can see the potential trenches in action so this is the trenches this is the lines that we need and this is the connection right into the building and these one out here they have been calculated on the basis of this other data that we've seen before so that was a long talk and i'm coming to an end this is the team currently it's changing every now and then because we're in an agile process there's terrestris mondialis metaspatia funhofer geomare camp2cam this is all open source guys and there is contera that's an esri company which is okay we can work with them together so it's a conflation of different technologies thanks a lot to the team and thanks a lot for your attention and i hope you can enjoy phosphor g 2021 see you next time bye bye okay so thanks a lot anul for your great presentation showing the platform and the challenges that going on here big applause for anul please and as i mentioned before he cannot be here so you can meet him in the chat and if you have questions you're welcome to ask questions there so one question was posted in the questions section i forwarded it to the chat and have a safe journey anul and hope to see you in person at the next conference and all the best and we are looking forward to the next presentation in a minute so stay in our room to hear about traffic in Buenos Aires shortly
 Hello, so welcome to the next talk.  It's a talk from Arnov Crystal and he should be here now and joining us live, but unfortunately  he can't join.  He's in the train.  I'll show him for a minute, but the internet is not so good there.  So we have his talk recorded and will broadcast it soon.  And Arnov will be in the chat so you can meet him in the conference room and ask questions  there.  So his talk is the Borg supply change and it's about the telecom and how the telecom  brings force projects into stable production.  Some words about Arnov before we start.  So most of you might know Arnov Crystal.  He was one of the founders of OSGEO in 2006.  He is an OSGEO advocate and co-founder of OSGEO.  He's working at Terrestris at the moment and he is a senior consultant there for telecom  AG.  He is working and on the other side he has metaspatial, the platform to coordinate smart  mapping and he is a scrum master also.  So welcome to his talk and hope to see Arnov in the chat.  Hello, my name is Arnov Crystal and I should be in Buenos Aires now, but unfortunately  it's not possible.  So I wanted to do this live but again this is not possible because they just cancelled  my night train.  So I have to take an earlier train and I'm not really sure they will work over the internet  connection that we get on the train.  So better record this.  Sanctinia posters, Geo server, Shogun, open layers and many more goodies in the cloud.  It's a talk by Arnov Crystal also known as 7.  What are my roots?  I am a carpenter and former director of president of OSGEO, entrepreneur and Buddhist scholar.  I am Borg, so beware.  My affiliations I'm working for Terrestris, GMBH and CoKG and metaspatial.com which is  a consulting or agile product management.  They are the Raster guys, Terrestris is the Vector guys, they do the web viewing and the  operation and obviously Borg Inc.  So if you have an issue with artificial intelligence please come to me and I will help.  I wanted to do this live but again this is not possible because they just cancelled my  night train.  So I have to take an earlier train.  So I summary this this year and wait for my second business like the also oflar, which  My affiliations, I'm working for Terrestris, GMBH and CoKG and Metaspatial.com, which  is a consulting for Agile product management in Mundialis. They are the Raster guys, Terrestris  is the Director guys, they do the web viewing and Mundialis the operation and obviously Bork Inc.  So if you have an issue with artificial intelligence, please come to me and I will help you.  So we have nine points in an open source tool chain and we'll look at them. So first question  is what do we want to do? Next, where are the pain points in the current way with doing things?  And we design a new process, I will show in a prototyping demo what is going on.  Then we find the right open source tools to make it better, decide on an architecture,  putting the pieces together, results in a demo again and seven of nine just because I had nine  points. So I thought I should add this ninth point. What do we want to do? We want to roll out  fiber optics in all of Germany because Germany is too slow. We have no fiber optics due to political  strangeness in the late 80s and 90s. So everything we have is copper, which is nice but slow.  And therefore we really have to speed up the planning process, otherwise we're going to be lost.  How to do that? What are the tasks? What do you need to do to roll out fiber optics? First you  need to collect data and then you need to analyze and process the data and use the data to position  the distributor boxes, which need to have a certain distance from each other, then find  potential trenches where you can actually put the fiber optics and generate permit application  forms because only then you can start to dig and actually put the fiber optics into the ground.  And all of this planning process should be automated.  Where are the current pain points? It currently is an on-site manual planning, so that means  people actually have to walk to a place and take pictures of potential positions for collector  boxes. And there is limited scanning options because you need so many people to drive around  and do this that it's just not possible with the current people we have. There is still a  lack of data because Germany is not fully open data and we have legacy systems, as you can imagine,  and a big TataCom provider in the basement. There is really old stuff running there.  We have waterfall project management and maybe we need a new mindset. This will be a little  excursion. I'll go into the manifesto for agile software development, which they have decided  is the right way to go forward. And what we do here is we put individuals over interactions,  pardon me, individuals and interactions over processes and tools. And we need comprehensive  documentation, but we think that working software is more important. We want customer collaboration  over contract negotiation and we want to respond to change instead of strictly following a plan.  That is, while there is value on the items on the right, we value the items on the left more.  And as you can imagine, this really is a new mindset and for a big old company with many,  many people and lots of hierarchies and bosses and stuff, it's not easy to implement. But I think  we succeeded well. So let's go to the prototyping and have a look at what we have. And this will be  a kind of live demo. So I'll switch to post-cubius. And we'll see what you always see when you start  with GIS project in the wild. It's an open street map WMS. And just to show this is Hamburg,  a few data that we use for input, we obviously need the buildings we want to put the glass fiber.  And we need to have the landlord and land parcels. And then we need to know who owns which part.  So this is probably public and this is probably private and all the other ones, we're not quite  sure. So there's some risk in doing that. But that's all the data that German data is currently  showing us. So this is part of the basic data that we need. And then we have  more data, which is a lot more detailed. That's this data. And this has been collected by a  surface car, which actually actually runs on the ground. And we've seen this before. And these  are objects that have been detected by KNN, which is the Kunstgesnarl-Wonelles-Netzwerk,  which is an artificial neural network. And this is also detected different surface types. So we  have objects and we have surface types. And this is great. And all the planning areas that we  are going to use have been traveled with this surface car. But it's not enough because as you  can see in the back here, no car can drive there. So we have no data. So additionally, we have added  orthophoto classification, which complements the KNN data here. And you can see there is some overlap  in the data, but there's also some differences. So obviously, we also have orthophoto data. Let's  take out the other data so that we can see it. Here we go. And if we go into a detailed situation  like this, then we can see that the KNN data is quite detailed here. We can see this is asphalt  and this is concrete data. And these are the, let's take out the digital orthophotos again.  These are the floors. And what we need is the trenches. And the trenches have to have a certain  distance from the outside of the places in connection to the, where are they? You can see  this is really a slow process. If you want to do it in an animated, this one here, this is the road  area. And the road area from the road area here, you have to measure. And this is going to be,  where is my measure tool? I lost it. There it is. This needs to be around 40 centimeters. So from  here to there, it's around 70 centimeters. And that's the distance which needs to be taken from  the outside of the building. And the building is going to be here. Can put building here. So it's a  conflation of building data and of cadastral data and of road data. And all of on top of this,  you can now see in the orthophoto how it all comes together.  Come on, orthophoto. There you are. Okay. So let's go back to the presentation. And after the  prototyping, we know what we're doing. We're trying to position those collector boxes. And  we're trying to find out which is the best place to put trenching. We need to design the new process.  Because what we do in the trenching is we collect data and collect even more data and  collect even more data and then conflate this and process this and calculate and automate everything.  What do we have to calculate? We have to calculate what the cost of these different lines is going  to be. So if we look at the different costs here, and you can see that depending on the surface,  it's more expensive or less expensive to dig a road there, to dig a trench for a fiber  cable there. So this information is basically deciding then which path you're going to take  when you dig your new trenches. Okay, data, collecting data, open data in the states of  Germany is a little not so nice because there are some of them is open, some of them is partly  closed, some of them is really closed. And so it's a little difficult to get an overview.  In places where we can't get any data or we don't get any open data, we obviously go to take some  open street lab data. And where we can't get those for the processing of the surfaces, we also need  orthophotos. And then we have this surface car which drives around and has cameras here for panorama  pictures which we'll see later. And also a lighter scanner at the back and a high precision GPS so  that we will know where we are. Okay, now we have the data and we need the tools. What tools do we  need? I think this is the easy one. CuriosityChef's Zine, it's great for prototyping ETL, GDR,  GDR or GR and all other tools. And yes, we have some FME in there. There is Grasse and Dictinia,  there was an Dictinia workshop in the beginning of this conference. Provisioning happens with  G-server and GeoNetwork and viewing and planning is done with Shogun fiber maps which is based on  open layers and operating the whole thing in CI-CD with Kubernetes, monitoring Kibana, Grafana and  all the other stuff you get in the cloud. So let's look at the spatial data infrastructure and there  is some OSM data coming in here with the OSM importer into a post-gis database. This is like our  core vector database. That's conditional server data, compared with post-nase or with FME to go  into the post-gis database and is provided with GeoServer as a Geospatial service. The metadata  is collected by GeoNetwork open source and metadata and later on fiber maps will use in the  web client this metadata to actually access this service to get to this data. So it's all connected.  If you look at it at the processing perspective you have T-surface data which comes in through the  Kunstlis-Noronautis-Netzwerk which is the artificial neural network which will produce  outcome data which is resulting in the classified surface cover and the same for all the photos  which are calculated with GeoCutinia which leverages grass modules to actually do this  and all of this if it is a vector result also ends up in the post-gis database.  Next is the planning and distribution boxes and potential trenches and this happens in fiber maps  which is a web client application which accesses these Geospatial services which take the data  coming out of here and actually also can remote control actinia which can then pull new T-Food  surface data through KNN, FME classified surface cover put in the results here so that fiber  maps can continue to collect this because we don't calculate all of Germany all the time as you've  seen it's very detailed data that would probably take half a year on very big machines and then  the data is already outdated. So this is an on-demand processing change.  Okay designing the architecture the OOTC telecom cloud was a given no Azure no AWS it's  OATN telecom cloud and we had a red-edged open shift which is currently transitioning back to  Kubernetes we have a GitLab and Ansible and Puppet and we provisioned the whole thing with  Terraform which has been replaced by Steep recently the whole thing is deployed with Helm and if we  put the things together it looks like this we have a setup in workbench in GitLab and open  telecom cloud with open stack then we provisioned with Terraform lots of virtual machines in these  we have red-edged open shift with master infrastructure nodes and bastion IDMSFTP proxy  and fdb and duty b these are configured with Ansible and Puppet and then we deploy them with  Helm so that we get Docker images and the whole thing is CICD pipeline where devs push stuff  into the app repository which is compiled unit tested image build pushed image into the magenta  trusted registry and on the other side the ops push the prod and staging SDI which comes from  the pulled images into dev and staging a prod because deploy diff and lint so the whole thing  is running with a workbench in GitLab it's really a nice setup but it took about two years and I  don't know how many years of work in person days to actually get this up and running so fiber  optics rollout planning on speed and this is going to be a small live demo and after that I should be  so this is how it looks like when you plan these things and all of these little triangles and green  dots and gray dots are collector boxes and we're going to now move these collector boxes  and this is the T surface data which has been collected with lighter data and panorama images  and we'll have a look at one of those panorama images here so we start the standout sicherel  which is the place where you can put the collector boxes and you can see that there is some images so  we want to have a look at the image in the real world so what we do is we move over to fiber d3d  this is the position down there where this one is we're looking at and when we switch to fiber d3d  this is a different web application it's fired up now and it loads the point cloud and panorama  images and positions this object in the middle of it so this object is actually a vector data image  which is projected into the panorama images at the right point this is it you can move around in the  image and we can also there you can see the cone of the direction where we're looking at and if we  zoom into the picture then we will also see now we're zooming into the picture that the cone is  adjusting its size and direction to show where we're actually currently looking at so we always  have the orientation in the map if we move to a different position we can just click there and  we'll see a different view of the next road crossing that we are here and this is it and I'll stop  here for a moment because now you can see the t-surface card this is the t-surface card which  takes the data with the lighter scanner and the cameras taking the pictures and for a moment you  will see now the point cloud there it is and then all the pictures which are stitched on top so that  you have a nice 3d view now we will go back to the box that we have just looked at and we will move  the box to a new position it's wrongly positioned in the garage so we will take it out and put it  inside outside of the garage again there we go I'm sorry that this is a recording but I was afraid  that it wouldn't work for presentations not nice so once we've positioned it there we can go to the  next step and store this information if we were happy with the position now we can take pictures  this is like walking there and actually taking a camera and taking a picture of the place where we  want to put the box but the box is already there which is pretty cool and for the permit we need  different perspectives so we move a few meters onwards and then turn around and look better at  the box and take the next picture and again it's like positioning we can move it we can even zoom  in or out whatever we need to get the right perspective for the permit which we will let  it produce here you can now see the old and the new position and from where it comes we want to  save these pictures so now we're processing the pictures and putting them back in and here they  show up so this is our processing interface and now we want to make a report so we select this  collector box to produce a report and this report is the PDF which gets sent to the public  administration which will then evaluate whether the position of this box is allowed at the current  phase where it is shown so once you get the PDF you can open it up and you see here that you have  a number the postal code the name of the address and this is the box there is quite a few different  sizes standard sizes of boxes that you can position and you have an overview map and this is the two  pictures where you can see the before and after and before and after of this position and this is  what actually is enough for the community to allow whether to put this box in the corresponding place  or not so let's fast forward to another perspective this is now looking at another place in where you  don't have positioning it's just you can you go any place that you've traveled it's like google  street view it's not much of a difference i think the quality is much more detailed obviously so  then let's have a look at the planning process itself so this is the planning process and if we  go further zoom further in and we can see the potential trenches in action so this is the trenches  this is the lines that we need and this is the connection right into the building and these  one out here they have been calculated on the basis of this other data that we've seen before  so that was a long talk and i'm coming to an end this is the team currently it's changing every  now and then because we're in an agile process there's terrestris mondialis metaspatia funhofer  geomare camp2cam this is all open source guys and there is contera that's an esri company  which is okay we can work with them together so it's a conflation of different technologies  thanks a lot to the team and thanks a lot for your attention and i hope you can enjoy  phosphor g 2021 see you next time bye bye  okay so thanks a lot  anul for your great presentation showing the platform and the challenges that going on here  big applause for anul please and as i mentioned before he cannot be here so you can meet him  in the chat and if you have questions you're welcome to ask questions there  so one question was posted in the questions section i forwarded it to the chat and  have a safe journey anul and hope to see you in person at the next conference and  all the best and we are looking forward to the next presentation in a minute so stay in our room  to hear about traffic in Buenos Aires shortly
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