 So, I'm glad to present some Earth observation examples today. Yeah, so cities face lots of challenges due to climate change, obviously, as everybody knows. And we have a lot of difficult tasks to fulfill, so one thing is that we need to monitor the change, so we need to know what is changing, where is it changing, and how fast. We need to do a risk assessment to better plan mitigation measures, for instance, for heat risk assessment. We need to do a better infrastructure planning to build a livable environment in our cities. We need to better manage natural resources and lots of tasks more, and for all this, we can use Earth observation data. Since only 20% of you are familiar with observation data, I would like to show some data sets that we mainly use in our daily work. So, we use free accessible data from NASA and ESA, like Landsat and Sentinel Copernicus data. They have a big advantage because they are free, and they have a very high timely resolution. We also use, of course, very high resolution data, like aerial photographs or laser scan data. They have lots of advantages too, but they are mostly not free, and have a quite low timely resolution. So, one example that I have brought today is the assessment of lens, lens surface temperature. This is a very important indicator in terms of heat risk assessment and heat adaptation. And here you can see on the left side is the daytime summer temperature based on Landsat thermal infrared data, and on the right side, it's a night assessment of the summer temperature. It's based on modus. So, hopefully in the near future, there will be lots of sensors that will be able to monitor lens surface temperature in a very high temporal and spatial resolution. So, let's wait for that. But what we can already do with the data available, we have a very long archive. So, Landsat is operating since 1985, and we have calculated a simple trend in the development of lens surface temperature in summertime, and here you can see that the spatial resolution is already quite okay. So, this is an example from the city of Leipzig, and the northern red blob is a new car plant from Porsche, and the surface temperature has risen quite a lot in this area, where in the south there are big form mining areas refilled with water. So, against the normal trend of increasing temperature, these parts are a little bit cooling. These data we can use to create heat action plants. So, we can monitor and also have real actual heat action maps. So, this is a very nice application or use case. So, we can also monitor adaptation measures. We can see if the measures that a city undertakes are successful. Another use case, a very important one, is the monitoring of vegetation. So, we have lots of indicators that stand for thermal release. So, we have a canopy cover who provides shading. We have the vegetation height, which is important for green volume monitoring, for carbon sequestration monitoring. And all these indicators are very relevant for cities and climate adaptation, because of course they provide cooling through EVA transpiration. They improve air quality and help in decreasing noise pollution. So, these indicators are very important and will be even more important in the future to use a buzzword. It's a nature-based solution as planting trees. But we need to monitor the amount of trees and the amount of canopy cover, the amount of green volume. This can be done of course with very high resolution data, like aerial photographs and digital surface models. These can be derived from laser scan or from stereo image matching. And we built a model based on artificial intelligence. It's a unit approach. So, we can calculate quite timely and quickly those vegetation indicators. These data we use as a training set to upscale this on Sentinel-1 and 2 time series. Because we want to provide for the whole of Germany, for instance, every year the amount of canopy cover, green volume and also other indicators to be able to really monitor in a standardized way these really relevant climate adaptation measures. We tried this for a use case in one city, Duisburg. And we have very good results in the city of Duisburg. We didn't take into the training data set, so it's a complete external evaluation. And the airborne result is of course of very high quality and the spaceborne result is also really satisfying in our view at least. And we wanted to see if we can do a real monitoring because with every model you have a lot of noise. And the goal is to minimize the noise so that we can monitor real change and not have artificial change. And we were quite satisfied with the first outcomes even though the change in the city between 2018 and 2022 was not that big. So, most of the changes were less than 3%. Still the relationship is quite good and we can see that we can use the data for real monitoring. Here you can also see the airborne results on the left side and the satellite spaceborne results on the right side and you can see that the differences and rankings between the different districts are quite the same. So we will be using these indicators and models to do fine scale, medium scale and large scale monitoring of urban green. But we are also interested in the vitality of the urban green since shading and evapotranspiration and cooling can only be provided if the trees are healthy and what's quite interesting for instance in Sentinel-2 we have lots of spectral bands and those spectral bands provide information about leaf pigments, cell structure and leaf water content and if we combine all this we can derive vitality indicator telling us is the tree still intact or not. And here you can see an example from Potsdam. I don't know, is there a laser pointer? This is the famous castle of Sonsouci and it's a really nice UNESCO World Heritage Park with very old trees planted 200 years ago and many of them suffer quite severely from the drought in the last years and as we can see we can use Sentinel-2 data time series to assess the damages and the changes in the tree cover. We also can combine this with the street tree disaster so we can assess the exact date where a shift has been recorded and you can see that there's permanent damage. Other indicators that we will be incorporating into the overall model will be soil sealing, soil moisture and albedo and all this will go into a big regression model to assess which of the indicators are the most important ones to provide leverage against urban heat. All the indicators will be tested with the city climate model so we can easily change parameters so we can take off all the trees, we can plant more trees so we can have scenarios and test the effect or the amount of effects those indicators have and what's quite interesting for instance is that there is a very high correlation of the surface temperature with the perceived temperature which is really important for the assessment of thermal load for the people. All these indicators will be combined in heat vulnerability model where the exposition and the sensitivity of the population in the city will be incorporated so that urban city planners can assess their measures and plan for scenarios for the future. Our goal was to support municipalities in their climate resilient urban planning and I think I hope I have shown you some useful use cases how you might in the future use Earth observation data for your work hopefully. And for further reading you are invited to visit those websites and we also have a booth.