 Hi and welcome to InterGioTV. My name is Denise Wenzel and I would like to welcome you to today's Tech Talk with Bo Legier, a Director of Imagery and Remote Sensing at Esri and Peter Becker, Group Product Manager for Imagery at Esri. Welcome Bo and Peter. So hi, where are you joining us from, where? So I'm actually in Redlands, which is the headquarters for Esri, so I'm in California. Okay, Bo? And I am in the Denver, Colorado Regional Office for Esri. Great, so thanks for being our guests here at this InterGio Tech Talk. And today you have the opportunity to present your solutions or innovations or just what you want to share with us, with the InterGio visitors. So and first of all to our audience, I want to introduce our guests. So Bo Legier is Director of Imagery and Remote Sensing at Esri and his team works closely with partners and customers on their imagery data and analytics needs. And he has over 25 years of experience working with commercial and government customers to solve problems using remotely sensed data and analytics. He specializes in advanced data sources such as hyperspectral data, SAR and LiDAR. Bo enjoys working closely with customers to create solutions that bring together data sources, software and analysis to deliver insights from imagery that drive business value. And with us is also Peter Becker. He is Group Product Manager for Imagery at Esri. He joined Esri in 2005 and has over 30 years of experience in the geospatial and remote sensing industry. He has focused on Esri's imagery technology and scaling these capabilities in the cloud but also has extensive knowledge and experience in the many facets of applying imagery to mapping and GIS. So please explain so why image data and especially remote sensing data find their way into the GIS systems, GIS systems at all and for which applications does it make sense and I don't know, who wants to start, Bo or Peter? I'll go first, let me take that first. So a couple of things on that, I mean first, a lot of people consider GIS really the analysis of vector data and it really isn't, it's really the analysis of all forms of geospatial data both in place and time. Remote sensing really takes lots of aspect, it's not only the optical imagery but it's lidar and radar and all forms of data which is sensed and really it still is the primary source of needing all spatial data. So in the past we've really seen, we've seen people have seen GIS and remote sensing as being really two different disciplines which are typically used, different tools and different environments and they really did not benefit from each other. The GIS people would typically only see imagery as a background and the remote sensing users would really only see imagery with really very little context about the spatial extents and the influence of other layers that might have in the analysis they're doing. And I still, amazingly that I still sometimes see users doing remote sensing as a single data source without actually using the other layers to provide that context and explanation. So in our GIS we've really removed this distinction across the board, you know our primary desktop application which is ArcGIS Pro is really designed for GIS professionals but by that we mean across the board both people doing vector data but also a lot of imagery data and we've really added extensive capabilities for processing and advanced analysis of all forms of imagery. So I mean you know the interfaces tools work both with the vector and the imagery in a single environment and provide really a unified experience. And similar on the servers and the servers perform the processing of both types of data. They handle the massive volumes of both vector and raster data simultaneously and you know a lot of the more advanced image analysis actually involves the use of a lot of vector data such as the location of plot boundaries or roads or streams and things like that. So the integration of this data is critical for different analysis and we're also seeing you know a lot of the outputs of image analysis actually being vectors. If we take you know the AI and deep learning as an example where you know massive volumes of imagery are now being processed and analyzed within ArcGIS and the majority of the output of that is actually vectors that also then need to be further analyzed to really extract that useful information. So it really gets you know the value of GIS is really of performing the processing analysis on both remote sensing and the vector data combined. So I've mentioned sort of the higher end professional desktop analysis applications but this also goes you know to the same thing as I said on the servers but if we look into how these applications are becoming available to most people they're becoming available in web applications or in field applications and that's also an area where we see that while in the past people have really looked at a web application and seen just sort of imagery as a background context we're seeing much more now is that users really wants to understand what's going on behind the data so the ability to integrate that imagery and remote sensing data sources into those web applications is really allowing a much more dynamic applications for users to go anywhere in the world look and really understand the interactions that are happening between these different sources and that's really where the technology that we have is very powerful in being able to not only display sort of static data but also really work in dynamic environments enabling the users to interact with processing literally in real time with the processing performed on potentially massive amounts compute in the background so really it's the fact that the integration of the remote sensing data and the GIS data into a complete platform is really opening up a lot of new capabilities that were previously sort of siloed into different areas hope that helps a little bit. It helps a little bit. Do you have anything to add Bo at the moment about these applications? No, I don't know not at the moment. Okay. But I will later. Okay so let's go a little bit deeper because the issues of sustainability and climate change affect all of us and what can data from remote sensing do for the climate protection and the sustainability. Just explain this to us and give some examples. Okay, I'll take this one as well for the time being. So yeah, I mean, Esri is really dedicated to understanding the environment. I mean, we really work incessantly to, you know, essentially measure and model the world and allow us to really understand what's what's taking place and geospatial technology is really critical to understanding the world. You know, it's really only through understanding what we have can we hope to really hope to change what we're doing in the future and see where we're going. So I mentioned before, you know, we provide the tools to take the billions of measurements, you know, manage those measurements, share them, refine them to create products to help understanding. You know, perform the analysis on them and then enable those results to be visualized and shared to others for them to understand. So this includes the sort of processing of petabytes of imagery and remote sensing data that can then be directly analyzed and used for different analysis. So the tools that we provide, you know, work on the management of the data, the dissemination of the data, the processing of the data, the analysis of the data and the visualization and exploitation of the data. And when we look at, you know, sustainability and the issues of climate change, it's important to really understand time. You know, time has really become the critical dimension and much of the work that we've been doing over the years is really to help work with time. We've added a lot of tools, for example, for working with multidimensional data. This has been able to take the huge amounts of scientific data that's being created and enable us to visualize and analyze it to really understand the trends that are going on within the earth. So this is taking not only scientific data sets and looking at the trends and those, but also taking remote sensing data and what sometimes people refer to as analytical ready data, which really stacks of data sets such as Landsat and Sentinel and being able to look through those and analyze those four different trends for these trends. So we really need to, you know, to enable sustainability, we really need to understand where we are in where we are now. But more importantly, or just as importantly, we need to understand where we wear. So this aspect of being able to take all this data that we've been collected over the last 40 or literally 100 years, being able to look at that, identify the trend where we wear, identify where we are, and therefore we can predict better for where we are in the future. So these are really the tools that we are working on to analyze these data, understand the data sets. And it's not really about doing all the work. It's really about providing those tools that really allow the world, so many people, literally millions of people in the world who use RIS, as we technology, to take those tools, work with them to help us understand these problems and then share those with a wider community to understand where we are and where we're going. So really it's really the combination of all those, including also providing access to that data sources to enable that analysis to be performed. So that's sort of how we play in that whole sphere. Thank you very much for that overview. This is really interesting. And let's go to the other topic, trend topic of our time, which is smart cities. And it's all about the creating of digital images, digital twins of the cities to understand them better and to guide processes and citizens in them to put urban planning just in a better, understandable environment for everybody to participate or to create. So what can that remote sensing or those images achieve in this environment? How can they help with the urban planning or just the participation or just give us those examples, please, or go into that topic deeper with us? Oh, yeah, I'm going to take this one. So, yeah, smart cities is definitely a trending topic. And really to understand smart cities, you have to understand, you know, what is driving this movement and it's really being driven by the digital transformation of city and regional planning. And that starts with the actual planning and doing that in a digital way, doing that in three dimensions and building models of what your city should be or is or would like to be or some additions that could happen there. And in that case, you know, we embrace technologies that allow for this type of urban planning and also 3D modeling. We have products such as our city engine product and our RTS urban product that do that. They embrace the planning aspect of the digital transformation that the city's desire to get to this end goal of smart cities. Now, once you have done that planning, that's when the remote sensing and imagery can come in to get a visualization to derive information from the city as it is built and as it exists. And this is where you bring in, you know, aerial collections. Sometimes those aerial collections are flown by drones. Some are flown by aircraft with very capable high altitude cameras. And those remote sensing data sources can be used to create realistic three-dimensional models that are the form that we're talking about are 3D meshes. Once it can be put onto a web app or a map or into a planning solution and you can actually see the as-built city, these are models that we build either on the very local scale at a site level from drones, from our site scan and drone to map products or on a city scale from our shore product line. And these data are highly accurate. They are visibly accurate. And a lot of the times they create or they contain the attribution that feeds directly into the GIS planning function. So this feels natural for an urban planner who's used to using a GIS to do this, but sometimes only in two dimensions. Now the nice thing about remotely sensed data sources is they come in various forms and there are other forms of imagery that are not flown overhead that could be driven on the streets with cameras mounted in cars and taking imagery in 360 degrees, you know, from all angles, a camera literally going in a circle above a car. And this provides that on the ground detail and we call that oriented imagery. And that integrates perfectly into a GIS and it adds another dimension to the smart city, to the digital twin instead of just the highly accurate 3D model of the building. Now you have the street view of the building or you have the ground features, the street furniture, things that can be captured from one of these cars and we add location to that data or we take the location from that data and put it onto a map. And another source that we can also bring in is LiDAR. LiDAR has been around for quite a while. LiDAR can be taken from the aircraft, it can be derived from drones and it provides yet another source that can be either, that can be used to model buildings, that can be used to accurately map the ground and the features on the ground. So all of these combined into a, you know, the smart city experience. Again, it starts with planning and goes to the capture of that environment as built and it's all enabled by technology that exists today inside of the GIS. Thank you for these examples how GIS or imagery is used to help or to build smart cities. And just to summarize and illustrate how can organizations use GIS to address the current challenges like climate change and sustainability or just once again, how is GIS used to help to build smart cities? Okay, so I mean let's have a look at the, you know, ever increasing so-called natural disasters that seem to be, you know, that are happening in the world and unfortunately increasing all the time. You know, in the last years we've seen, you know, terrible floods and fires literally sweep or burn away complete towns. You know, we have to realize that the world is changing and, you know, these disasters are going to continue more and more as we move forward. And to do that we really need to prepare ourselves and what we mean by prepare ourselves is not just to build things to try and ward off these issues but really to try and model them because they will keep coming. So, you know, we really need to plan for what to do in the case of these events. You know, where should people be evacuated? What happens after the events? How do people get compensated for their losses and helped? And this really, to do this we really is a spatial problem. We really need to ensure that we have really good models of our environment. We need to understand where these assets are. We need to be able to model what happens when these effects. So, and that really is all about, you know, collecting this GIS on spatial information, managing it effectively, sharing it with the appropriate organizations so that we know, you know, where is the water? Where are the electricity? Where are the schools? Where are the hospitals? These are all spatial problems that need to be solved. But they need to be solved together. It's not about solving individual problems of where a particular asset is. The real value comes into bringing these multiple assets together. And this is really where the GIS comes into its own. It's the ability to bring the data sources from within your own organization but then also to bring information from other organizations. So, in other words, to collaborate with organizations within a city so this information comes together to create this real living model of the environment which in a way is this digital twin that we can then really use to understand and plan what could happen and what we should do to better prepare ourselves against it. So, you know, the key thing is the way that the GIS technology now can bring together these different sources and really enable us to understand and better prepare and plan and hopefully also understand what we can do to really change the course and really create sustainable economies that humanity can continue to grow appropriately but still ensure that nature is taken into account because otherwise it will consume us. So, I don't know, do we also want to talk about how exactly does integration of image data and remote sensing data into GIS work exactly? So, you have some concrete solutions you want to explain to us or to the audience to introduce visitors right now? Yeah, I'll take that and I'll talk about some solutions. The integration of imagery data and remote sensing data in the GIS really starts with a base map in most cases. So, a lot of the times the first exposure that a GIS user has to imagery data is a high resolution imagery base map that can be brought into the GIS and is most often provided with the GIS. That provides some context, some location context, what do things look like at a certain date, a kind of baseline of knowledge and information. Now that is often just the beginning and for most customers that's not appropriate for what they want to do with their solution. Often they want to create new information, vector-based information as Peter really dug into in the beginning of this talk. They wanted to arrive new vectors from data that is current or that is richer in its content. And in that case we bring in other sources. So, let's say for an example you wanted to get a view of an agricultural field. Well, you want to get a view of an agricultural field either now or very recently and you would want to bring in additional data and the GIS, our GIS is built to connect the data sources that are either served on the web or can be obtained and served locally. And then the solution involves applying an analytic to that data and deriving vector content and that is the simplest manner. That analytic can be very simplistic or it can be very complex. It can use image science technologies or it can use artificial intelligence and machine learning to derive features that are trained and can be extracted with high accuracy and high reliability. So, these types of solutions, this is kind of your standard GIS workflow but you can go deeper and take that two-dimensional problem and turn it into three dimensions and bring in lighter content and bring in the elevation aspect of a problem because elevation is critical to a lot of problems such as modeling water flow and other hydrologic phenomenon or just looking at a landscape and predicting its impact in a natural disaster and that's where you really want to bring in content that is not just two dimensions, it goes into the third dimension and reverting back to my earlier conversation about smart cities, a lot of the times you want a fully immersive 3D environment and that is bringing in more and more imaging and remote sensing content that goes way beyond just the baseline, base map. So, the imagery in GIS really just sometimes starts at the simplistic bring an image as a background to your analysis but a lot of times goes much deeper than that and it involves deriving additional, more timely, more advanced information from those imagery sources and the key is getting that imagery data into the system and then applying and then having the right analytics to apply to it. Thank you. So last week we had a talk about the climate change and it was tracking climate change with geodata and Jürgen Schumacher from Esri, Germany was here in our talk and he referred to the living atlas of the world. So who does this atlas help and what is it exactly? Well, thank you for bringing that up. It was actually, I enjoyed that presentation earlier. So yeah, let's talk a little bit about the living atlas here. Jürgen actually mentioned it and maybe I can go a little bit more into details of what it is. Really, it is a lot of curated content that has been put together by Esri so it provides access to a lot of authoritative data sources that can be very easily integrated into all forms of applications. So these data sources can include a lot of vector data such as countries and road networks, a real-time traffic information, vegetation health. A lot of the data sets are imagery related. One of the key ones is the world imagery base map and that really is a mostly submeter resolution maintained accurate image of the world that is we keep up to date and that's used extensively. I mean, that's accessed more than a billion times a day as a base map being used in lots of applications. There are other data sets in there including, for example, Sentinel-2 data. That's all Sentinel-2 data is accessible both as top of atmosphere as well as surface reflectance that can be used. Landsat data, it includes, for example, world elevation. World elevation is a data set that's been collected from literally hundreds of data sources, organizations around the world contribute their elevation data to ArcGIS online so that it basically creates this global elevation model, which it's not a single data set, it's actually made up of lots of different data sets and if you go to the world elevation, you can go to any particular location and actually see the individual data sets that may contribute to the final results. So these data sets are not data sets necessary that as we collect, we don't go and generate those elevation data sets, for example, we collect the data sets from different areas and compile them into these sort of complete data sets which are properly attributed and documented so that they can actually be used. So yeah, in the imagery side, remote sensing side, I'd mentioned, things like the Sentinel and Landsat and the data sets like Nate. There are a large number of different data sets that we make available. We also make available different applications within the Living Atlas. Living Atlas really contains some applications that you can directly use that may help solve different problems and they also contain things like story maps. These help explain how trends have taken place. If you look at, for example, COVID and the development of COVID, you can find, for example, story maps about that. They also contain models and these are models that can be run within the ArcGIS platform or ArcGIS system, better said. So for example, these could be models to process remote sensing data. For example, pre-trained models, we've recently in the last year added to the Living Atlas about 20 pre-trained models which can be applied to perform deep learning against different data sets. So this could be to take, let's say, imagery data and extract footprints or take imagery data and extract road networks. So these are models that have been trained, curated from different sources and then made available to anybody to use directly or download from the Living Atlas. So the data sets that are there, most of them or a large number of them, can be basically free for anybody to use. Some of the data sets do require that you had a login to ArcGIS online and there are also data sets there or premium data sets which are provided by partners and we work with a lot of partners and very often help them provide their content to our customers or in some cases incorporate that content into the Living Atlas and make that available sometimes through subscriptions. So there's a lot in the Living Atlas. The best thing to do if you're interested in Living Atlas, just go and have a look, go and browse to it, just search for the Living Atlas of the world and have a look at the phenomenal collection of content that can be visualized and then integrated easily together to create different applications. So you'll see there it really fits in line with ArcGIS online which really provides that sass environment to enable you to create and bring together all these different data sources and create applications to solve real-world problems. Thank you very much for joining those ideas, those preview or insights into the Living Atlas with us. Yeah, this tech talk is also part of Intitude 2021 and maybe you also want to summarize again what you will be presenting this year, why we should go and visit Esri's booth at Intogio 2021 or also browse for the Living Atlas or just use the stage again to summarize again your great products, your imaginary works and yeah. Yeah, so at the conference this year, we actually have a hybrid booth together with one of our partners Esri Germany and we're going to be really focusing on imaging and remote sensing technologies in five video conference rooms with different topics and presentations. We are going to have staff that are doing presentations two per day on our shore technology and that's our 3D reconstruction technology that really powers the smart city trend and workflows. And on top of that, we're going to have an expo stage presentation by one of our members of our staff over in Germany named Conrad and he is going to present his vision for 3D modeling, smart cities and those workflows. So we are prepared to talk about almost any imaging or remote sensing topic but we really do have a heavy emphasis on our 3D capabilities at the conference and we invite everyone to come in and just get a demo of that or talk to one of our experts in imaging and remote sensing. We'll be there in a hybrid form. So this is it for the moment from Peter and Paul from Esri and the remote sensing and GIS live. You can also discover at Intergeo in Hanover and see you next time to the next Intergeo Tech Talk. And thank you very much. Thank you very much for having us. Okay, thank you. Have a good day.