MDAL: mesh data in QGIS
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| Number of Parts | 351 | |
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| License | CC Attribution 3.0 Unported: You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor. | |
| Identifiers | 10.5446/69140 (DOI) | |
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| Production Year | 2022 |
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00:00
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Information technology consultingRaster graphicsSet (mathematics)Temporal logicPolygon meshArithmetic progressionData managementPropagatorVector spaceComputer animation
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Asynchronous Transfer ModeEquals signInformation technology consultingDefault (computer science)Raster graphicsDenial-of-service attackComputer animation
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Information technology consultingFunctional (mathematics)Hand fanMaxima and minimaCalculationAdditionPolygon meshRaster graphicsQuicksortComputer animation
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Cloud computingPlot (narrative)Point (geometry)Series (mathematics)Profil (magazine)Graph (mathematics)Time seriesPlotterGraph coloringComputer configurationMultiplication signPlug-in (computing)Computer animation
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Information technology consultingComputer animation
Transcript: English(auto-generated)
00:11
And mesh data and mainly mesh data in QGIS, can you hear me all well? Okay, cool. About us, we are Lutra Consulting, we are core contributor to QGIS, some of the features
00:26
you might have seen like QGIS 3D, mesh data, point clouds and merging maps, we developed it all open source and focus of today is mesh data, what is mesh and why we
00:43
need that, the structure of data, how it looks like and the library and then the data formats and how you can use it in QGIS from the user point of view. So what is mesh?
01:04
Mesh it's data that it's not on a regular grid, it's not about point cloud, I put yet because there is a convergence, if you are following a point cloud development you probably have seen some mesh representation of the point cloud in QGIS which is available now but
01:27
this one is slightly different but there is a point where those two can converge. About mesh data, you usually have in QGIS you have vector data where you have geometry
01:43
plus database point line polygon, you can use the vector data to represent it, then you have rasters which you have pixels and then the values inside each pixels, it's regularly gridded and then you have unstructured data which is more representative of the
02:09
real world and you have data either interpolated or outputted from some numerical model which are not exactly very well defined regular mesh, it can be a triangular mesh, it can
02:31
be a quad mesh or any dimension and in addition to the data inside or at the vertices of
02:42
the mesh you might have some sort of extra information like vector information if you think of the velocity from a weather data that you define the direction and the magnitude so you can have this kind of data inside the mesh as well and to make it more complicated
03:03
you have got also time dimension to it as well so this data repeated and changes for each time step. There are some attempts to represent mesh in GIS world and usually people, if you
03:25
want to have it as a regular grid you convert it to grid or you create contours out of it to represent it as vector, the problem with this is the data is already
03:41
interpolated and when you do this kind of conversion you further interpolate the data more and you add more uncertainty or more approximation to your data and by the end especially for example for weather data or hydrological, hydraulic data, when you do this interpolation because it's valid at a certain point where the numerical model
04:06
has produced it then this interpolation makes your whole data completely invalid let's think of a flood map when you do the conversion if you do the contouring or gridding incorrectly suddenly you have got a whole bunch of properties in a flood
04:22
map or your temperature will show a different range for a whole city if you deal with a large scale kilometer size grids. So to resolve that issue what we decided was to create a library to read the mesh
04:45
as is and present the data at their own values right where they are instead of trying to contour them or grid them. The mesh data usually have data on cell
05:02
or face or on each vertices, they are with time dimension usually and you can have a scalar data or vectoral data attached to it and you can have multiple quantities if you think of like HDF or NetCDF you have a data set which contains temperature,
05:21
humidity, cloud coverage, so you have multiple data sets inside that data container in raster world you handle it with bands but in mesh data you have got some sort of quantity. So for that reason we created a library called MDAL and what it does you have MDAL
05:51
reads the mesh and visualises it in QGIS similar to Poudal that it reads the point cloud or GDAL which reads the vector and raster data and shows it in your, I'm
06:08
focusing on QGIS because we are shipping QGIS with this library but it's an open source library so you can use it with any other viewer. The
06:28
for Ugrid and data as I said you can have multiple versions of it if you think of a regular grid raster data is a specific type, a particular type of mesh data and you have complex mesh
06:50
data like this that it changes based on the topography for example in this case where it represents and these are the information about MDAL where it's hosted and it has become
07:08
an OSGO project as of last year, it has got capability to read and recently write
07:25
mesh data and started four years ago so it has different, these are the type of formats it can read and it can write to a couple of those formats as well, some of the most commonly used weather data and if you are familiar with
07:50
hydrological model or climate model these are some of the outputs from there, in addition to that MDAL also indexes the data so it helps
08:03
for fast loading of the data and it has got lazy loading so as I mentioned mesh data can contain quite a lot of time series so it indexes the data both spatially and also when you load it in QGIS it only looks
08:24
at the specific time step you are interested in, this is an example of mesh data in QGIS so you load your data, then you can change the style of the data, you can change the, if it
08:44
has got quantities you can change the colour, ramp similar to raster, also as I mentioned mesh data can contain vectoral components like velocity so you can visualise and animate the speed of those vectoral components as well, this is
09:09
a vector but you can have it as a trace as well, like this so you can create trace animations so this is oceanographic data and the vectoral component of it, how you can style it, this is an
09:33
example of again pollution data from Copernicus so it has got built in integration with the temporal management in QGIS
09:46
so if you have vector data, raster data and mesh data all using the same dataset you can easily animate your data and see the propagation or progress of your data
10:02
again it's using the default style of the data in your QGIS, if you are working with rasters it should be very familiar for you, this is some examples of the flood data
10:22
in addition we have got mesh calculator, what it does you can do very similar to raster calculator so you can add or do all sorts of calculations with mesh in addition you have some aggregate functions, let's say you have got temperature data and you want to, temperature
10:47
data for whole Europe and you want to search for or calculate the maximum aggregate for Firenze for July so you can do max aggregate and you can do spatial filter so it does both temporal and spatial filter to give you some aggregate functions
11:14
we have got also a plugin called Crayfish so you can create plots of the data, the
11:21
time series, also the long profile so you can see each colour represent each of those graphs we plan to add this plotting option now in QGIS directly so you don't need the plugin and also you can visualise it in 3D so these are the different components of it, MDAL for data
11:49
read and write, QGIS for rendering and data editing which I'll touch on in a bit and Crayfish for visualisation in addition to 2D data the focus so far has been in 2D data, mesh data can also handle
12:07
1D data if you think of like pipe network that can be 1D mesh data and QGIS can handle it MDAL as well and also 2.5D or stacked mesh, let's say you have temperature data for each
12:31
elevation so you can represent that in 2D by some sort of method of averaging between those heights, elevations
12:47
we have got no plan yet for fully unstructured mesh data but if you are interested we can have a chat animations of vectors and grids I showed you and as of last year we have added option to edit
13:09
mesh data so editing mesh data is a bit complicated but we have tried to achieve it in QGIS what it does, you can edit the values on vertices, split your mesh and do some sort of transformation of
13:31
the mesh so examples of it, let's say you have a mesh like this, you can split it this way and it creates rectangular valid mesh data in this instance and transformation for moving the data
13:49
and the moving it, if you haven't tried it in QGIS or there are some nice animations when you drag mesh vertices it drags all the vertices with it so you will get topologically correct mesh data at the end and it re
14:08
-indexes mesh so if you delete a face or add a new face the whole mesh will be re-indexed and again your topology will remain valid
14:22
The features we would like to add is data streaming, as I said the mesh data can be extremely large so what we would like to have some similar to cloud optimised TIFF or CAPC
14:43
or whatever the name is, we can stream the data eventually, so if you deal with for example at the moment what weather organisations are doing like ECMWF or WMO or Canada recently the weather centre
15:06
I've seen they stream the data through WMST so that you have got WMS with the time component so what they do, they convert their net CDF or grip data, the conversion I mentioned and then they create an OGC service based on that
15:28
which by the end for visualisation it's good but if you are handling the, if you want to work with the data and query the data, let's calculate the maximum, clip the data etc it's completely useless so with having some mesh streaming that allows you to achieve that
15:49
More formats and standards and some Python or R API so you can do your statistic easily through those popular languages and we have done point cloud to mesh data to some extent only for visualisation
16:11
if you have looked at the recent QGIS but it would be good to have some sort of proper tool, processing tool which my colleagues will talk about shortly and it will handle all the point cloud filtering
16:28
and by the end you have mesh data ready for your numerical model for things like hydraulic model, climate model etc So that's it and I hope you have lots of questions and I can answer them