 Hello, this is Liu Lu. Hello. Okay, I see my screen. Yep. Could you see my screen? Yes, we can. Okay, thank you. If you want to start your presentation and presentation mode, you can do that. Or just leave it like that. Hello everyone, thank you for your attention. Today my topic is about using error like this sets to analyze land graduated land substance. And this is my study area in Lark in the southeast spring. Land substance occurred in Lark at a rate of 10 centimeter per year being the greatest in Europe caused by grad water withdrawal. And here we, we get four kind of data. Leida insha, GNSS, and the soft soil sickness. And this is a geological map in Lark. And this is a physical velocity map derived from Leida and derived from Isha. This is the correlation and the differences between Leida and insha data. And we use the common space to analyze distribution of error affected by substance rate for Leida and insha results. And this is insha Leida and GNSS at 10 series at least GNS station. Notice here, the chain is opposite. This is because during 2009 and the 2011, here we built the new buildings. These new buildings, but insha and GNSS data is from 2011 so it's reasonable. Leida chain is opposite different. And this is a set is catered for comparison between GNSS and Leida and Leida and insha results. But here we only compare the L, O, R, C and O, R, C stations. This is the soft soil sickness. And this is the relationship between soft soil sickness and the physical deformation rate. And this is cross access analysis between the different results. We can see the ground deformation rate derived from Leida, insha and GNSS has a great agreement. But they have an opposite chain with soft soil sickness. And we can notice here, there are many Leida outliers. Sun is because like this, this is L, R, C, C station. We can see here, this is totally different, is totally different because of a building. This is our approach of Leida processes. First, we merge different Leida and the logisticion. Then we felt a non-ground, non-ground point of clouds. And the simple, the different point of clouds. And here we find many flight lines. So we use different cell height to remove the flight line. And then calculate the N3C to distance, get the differences. During this process, there are many incentives. The first one is the cell height and center of the prime clouds. The second is the residue non-ground prime uncertainty. And the center in point of cloud compression also will influence the results. Let's all thank you. This is my presentation. Thank you for your attention. Thank you very much, Lu Ru. Very nice. All right, so questions. I've got to get back to participants. Anybody? So Lu Ru, that's, you know, it's, it's really hard for us old people to actually make out some of the, some of the labeling on the diagrams. You got to pity some of us. Anybody question? Scientific question? I'll ask a question, Joe. Yeah, okay. I don't think I heard you say, is this being done with a commercial LiDAR? Yes, this is, this result is from the commercial LiDAR. So here I met many nurses. So before I ask the professor, William, I don't remember about the noise, how to remove the noise. Yeah, right. Okay, so Andrew's got a question. Andrew, do you want to unmute yourself? Andrew, I think you can. Okay. Yes. So I, with the images that we are seeing, I wanted to know whether they are ground based LiDARs or it is a satellite image that we are observing. Okay, maybe I, sorry, is it LiDAR or a satellite? LiDAR satellite, yes. Okay, thank you. Yeah, yeah, that was the GNSS. What, what is that Lu Ru, because you talked about it, he didn't. GNSS? Yeah, is that global? Yes, yes, GNSS is global. GNSS is like GPS. Okay, yeah. This is the thing. Okay, okay. Yeah. All right, well thank you. From William. I think the last question was, where is the LiDAR located? Is it on a satellite or where is the LiDAR taken from? This LiDAR is located in this area, area LiDAR. But this is a commercial LiDAR and this is processed LiDAR. So what I get is not the real data. So I met many, many noise. So I asked you how to analyze the noise. See, I see where you're coming from. My answer is I don't know. Okay, thanks, thanks very much, Lu Ru.