This guide is targeted at aircraft developers wanting to get started converting AC3D models to glTF models supporting PBR/HDR and suitable for 2024.2.

Caveat: The author is not an aircraft developers, and this guide will focus only on the main processes involved. It also assumes use of Blender 4.5.3 LTS. We are using as an example Emmanuel Baranger's Stampe-SV4 as a simple aircraft but with nice animations and materials. Thanks Emmanuel!

== Importing AC3D to Blender ==
See [[Howto:Work with AC3D files in Blender]]. For Blender 4.5.3 on Linux I had to install the importer to <code>$HOME/.config/blender/4.5/extensions/user_default/</code>.

By default, the .ac file will be imported with a "Principled BSDF" surface, and can be exported our as .glTF. See [[GlTF]] for details of the export settings to use.

== XML Changes ==
Obviously you need to change the model.xml file to reference the .glTF file rather than .ac.

You should also take the opportunity to set the appropriate -set.xml files entries.

Specifically, under <code><sim></code>.
<minimum-fg-version>2024.2.0</minimum-fg-version>
and
<compatibility>
<pbr-model>true</pbr-model>
</compatibility>
This will ensure that the aircraft is displayed correctly within the launcher.

== Animations ==
Unfortunately the axis for animations are different between .ac and .glTF. You will need to swap the Y and Z axis, and reverse the Z axis.

This is easiest seen by an example, and is fairly easy to do semi-mechanically by going through an animation file. Fortunately the X (forward/backwards) axis is unchanged, so more steam gauges should work unchanged.

Example: Aileron rotation using two sets of points:

2024.1.3
<animation>
<type>rotate</type>
<object-name>aileronBD</object-name>
<property>surface-positions/right-aileron-pos-norm</property>
<factor>-15</factor>
<axis>
<x1-m> -0.393 </x1-m>
<y1-m> 3.357 </y1-m>
<z1-m> -0.304 </z1-m>
<x2-m> -0.698 </x2-m>
<y2-m> 1.186 </y2-m>
<z2-m> -0.383 </z2-m>
</axis>
</animation>
2024.2.0
<animation>
<type>rotate</type>
<object-name>aileronBD</object-name>
<property>surface-positions/right-aileron-pos-norm</property>
<factor>-15</factor>
<axis>
<x1-m> -0.393 </x1-m>
<y1-m> -0.304 </y1-m>
<z1-m> -3.357 </z1-m>
<x2-m> -0.698 </x2-m>
<y2-m> -0.383 </y2-m>
<z2-m> -1.186 </z2-m>
</axis>
</animation>

== Effects ==
glTF by default has what's known as a "Principled BSDF" surface model [https://docs.blender.org/manual/en/latest/render/shader_nodes/shader/principled.html]

FlightGear glTF will automatically assign the <code>model-pbr</code> Effect, which replicates this, and should be used by aircraft. If you find yourself wanting to add an effect, talk to the core team - it may make more sense to enhance the model-pbr effect.

There is also a <code>model-transparent-pbr</code> Effect which should be used for transparent objects.

== Textures ==
PBR uses a set of properties of materials to render surfaces with much greater accuracy than previous rendering methods. As such, the use of textures is somewhat different. The main texture is purely used for the base colour of the material.

Blender Principled BSDF will take Metallic and Roughness values direct from the Material entry. However glTF (and the <code>model-pbr</code> effect) supports using an additional texture which will have (Ambient) Occlusion, Roughness and Metallic information in the RGB channels. This allows you to adjust the roughness and metallic values across the texture itself - for example to mimic polished areas of surfaces. It also allows much more naturalistic rendering of occlusion.

=== Ambient Occlusion ===
Blender can render a texture for ambient occlusion. It will use the same UV coordinates as the main texture, so it is important that you don't have overlapping UV coordinates. The option to bake an ambient occlusion can be found under Render on the Editor properties, under Bake. You will need to set Ambient Occlusion as the Bake type. I have found the Bake to sometimes crash Blender. One can work around this by baking only part of the model at a time, without setting the "clear" option so that the additionally baked AO is added to the texture.

It will also bake to a full RGB image, when what you need for an ORM texture is just the R channel. You will need to use an image editor (e.g. GIMP) to convert the baked AO texture to the R channel.

Blender support for Ambient Occlusion in glTF is a bit of hack on Blender's part, and you need to create a special node. See https://docs.blender.org/manual/en/latest/addons/import_export/scene_gltf2.html.

Howto:Convert from AC3D to glTF

2025-10-06T19:55:29Z

Stuart:

Howto:Convert from AC3D to glTF

2025-10-06T19:52:53Z

Stuart:

2024-06-10T19:06:27Z

Stuart: Add genwaterraster.py command

{{WS30 Navbar}}
This article provides instructions on how to generate basic WS3.0 terrain.

WS3.0 terrain consists of two parts:

# A terrain mesh consisting of a landclass texture draped over an elevation model.
# Line features such as roads, railways, rivers and coastline.
The terrain is generated by a set of tools that are packaged in a docker image for convenience.[[File:Diagram-export-21-12-2023-16 29 37.png|thumb|Basic WS3.0 Scenery Generation Process]]

== Prerequisites ==

=== Set up a Workspace ===
Create a directory with the following sub-directories:

* <code>data</code>
* <code>output/vpb</code>
* <code>output/Terrain</code>

=== Docker ===

# Install [https://docs.docker.com/get-started/ Docker] on your platform.
#Pull the docker image by running the following command

docker pull flightgear/ws30-vpb-generator:latest
Optionally, if you are hitting rate limits:
#Create an account on https://hub.docker.com/. (Note that you will need to click on an email verification link before you can log in for the first time)
#Run <code>docker login</code> before the '''docker pull''' command above

== Getting the base data ==
You need two pieces of data for the area of scenery you are generating:

# An elevation model (aka DEM). This indicates what altitude each point of the surface is.
# Landclass data showing what type of terrain is at each point of the surface. This is often either a Raster (effectively a texture), or vector data.

=== Elevation Model ===
Download the NASADEM elevation model for the area of scenery you wish to generate. This is available in 1x1 degree blocks from [https://lpdaac.usgs.gov/products/nasadem_hgtv001/ here], and with an interactive browser [https://search.earthdata.nasa.gov/search here].

Unzip the files into the <code>data</code> directory.

=== Landclass Raster ===
Download an landclass raster for the area of scenery you wish to generate.

* For Europe, use of [https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 CORINE] is recommended.
* For the USA [https://www.mrlc.gov/viewer/ NLCD] is recommended
* Sentinel-2 data is available for the entire world via [https://livingatlas.arcgis.com/landcoverexplorer/ ESRI], but has limited set of landclasses.

Put these into the <code>data</code> directory.

More detailed terrain can be created by modifying the landclass raster, and/or generating a new raster from vector data. These processes are discussed below.

== Generating Terrain ==
To generate terrain you need to run the tools within the docker container we installed above. The docker image is like a small, independent virtual computing environment running within your system. This particular docker image has all the scenery generation tools already installed.

=== Running the docker container ===
Firstly, get the container running from the directory containing your <code>data</code> and <code>output</code> directories:
docker run --rm --mount "type=bind,source=`pwd`/data,target=/home/flightgear/data,readonly" --mount "type=bind,source=`pwd`/output,target=/home/flightgear/output" -it flightgear/ws30-vpb-generator:latest /bin/bash

You should now find yourself in a bash shell within your container. You should see data and output directories which are linked to the directories you created earlier:
flightgear@ddcac77f7d5e:~$ ls
data output

=== Building the terrain ===
To build the terrain mesh, use the <code>genVPB.py</code> tool from inside the docker container.
Usage: genVPB.py <min-lon> <min-lat> <max-lon> <max-lat> <input-raster> [--reclass <reclass>] [--coastline <coastline>] [--hgt-dir <hgt-dir>] [--output-dir <output-dir>]
<min-lon> <min-lat> <max-lon> <max-lat> - bounding box of scenery to generated
<input-raster> - Input landclass raster
[coastline] - Optional coastline polygon data (.osm) to clip against. E.g. from <nowiki>https://osmdata.openstreetmap.de/data/land-polygons.html</nowiki>
[reclass] - Optional file containing a set of rules for reclassifying the raster similar to r.reclass. See fgmeta/ws30/mappings/
[hgt-dir] - Optional directory containing unzipped NASADEM HGT files. Defaults to '/home/flightgear/data'.
[output-dir] - Optional directory for output scenery. Defaults to '/home/flightgear/output/vpb'

For example, to generate a piece of terrain around Edinburgh (latitude 55.5, longitude 3 degrees West)
./scripts/genVPB.py -3 55 -2 56 ./data/uk_wgs84_10m_N54.tif
If you are using anything other than a CORINE raster you will need to reclassify the data to match the landclasses used by FlightGear. Those classes are defined in [https://sourceforge.net/p/flightgear/fgdata/ci/next/tree/Materials/base/landclass-mapping.xml Materials/base/landclass-mapping.xml]. You can reclassify them using the files in the [https://sourceforge.net/p/flightgear/fgmeta/ci/next/tree/ws30/mappings/ scripts/mappings] directory. E.g. to reclassify NLCD2019 data you can use <code>--reclassify ./scripts/mappings/nlcd2019.txt</code>.

genVPB.py will output the data to the output/vpb directory, in which you should find a series of files and directories.

=== Adding water ===
The terrain mesh does not have highly detailed water features - as typically the source data has a resolution of 10-25m. Water features are generated from OpenStreetMap data. To generate water features simply run the genwaterraster.py command.
Usage: ./scripts/genwaterraster.py <scenery_dir> <lon1> <lat1> <lon2> <lat2> [Cache]
<scenery_dir> Scenery directory to write to
<lon1> <lat1> Bottom left lon/lat of bounding box
<lon2> <lat2> Top right lon/lat of bounding box
[Cache] Optional directory to cache OpenStreetMap data to reduce API requests
The scenery directory should be your output/Terrain directory created above. For example, to generate water for the area around Edinburgh:
./scripts/genroads.py ./output/vpb -4 55 -3 56
Inside the ./output/Terrain directory there should be a set of directories and .png files.

=== Adding roads and railways ===
The terrain mesh does not have any line features - things like roads. These are generated separately from OpenStreetMap data. To generate line features simply run the genroads.py command:
Usage: ./scripts/genroads.py <scenery_dir> <lon1> <lat1> <lon2> <lat2> [Cache]
<scenery_dir> Scenery directory to write to
<lon1> <lat1> Bottom left lon/lat of bounding box
<lon2> <lat2> Top right lon/lat of bounding box
[Cache] Optional directory to cache OpenStreetMap data to reduce API requests

The scenery directory should be your output/Terrain directory created above. For example, to generate roads for the area around Edinburgh:
./scripts/genroads.py ./output/Terrain -4 55 -3 56
Inside the ./output/Terrain directory there should be a set of directories and, .STG files text files.

==Running FlightGear with the new WS3.0 Terrain==
To test the new terrain, simply include the output directory in your scenery path and run FlightGear with the <code>--prop:/scenery/use-vpb=true</code> to enable WS3.0.

== Advanced Techniques ==
The following sections describe more complex techniques to generate higher quality WS3.0 terrain. Almost all of them involve using different data sources to generate a more detailed landclass raster before running the final scenery generation processes described above. Generating a highly detailed landclass raster is where the magic happens.

Most techniques use gdal or grass to modify the raster/vector data, typically using the QGIS program.

=== Using a different elevation model ===
If you are using another elevation model other than NASAEM, then you may need to re-project it using QGIS/gdalwarp to the WGS84 CRS (aka EPSG:4326).

=== Landclass Data Requirements ===
For any landclass data we need to ensure the data is in the correct format. That means:

# Is a Raster (geotiff) rather than Vector data. This raster will become the texture on the terrain that the terrain shaders do their magic on.
# Uses the WGS84 Coordinate Reference System. The ensures that the terrain generation step is efficient.
# Has the correct landclass values for each terrain type. We use a set of values based on the CORINE raster set, defined in [https://sourceforge.net/p/flightgear/fgdata/ci/next/tree/Materials/base/landclass-mapping.xml Materials/base/landclass-mapping.xml].

Below is a quick table showing what steps you need to take for common landclass data sources.
{| class="wikitable"
|+
!Landclass Data
!Warp to WGS84 required?
!Landclass re-classification Required?
!Raster Simplification Required?
!Conversion to Raster Required?
|-
|CORINE Raster
|Yes
|No
|No
|No
|-
|CORINE Vector
|Yes
|Yes
|No
|Yes
|-
|NLCD
|No
|Yes
|Yes
|No
|-
|Sentinel-2
|Yes
|Yes
|No
|No
|}
Conversion to Raster must be done manually. Converting to WGS84 and the correct landclasses ''can'' be done by the genVPB.py script, but slows down scenery generation. Therefore if you are planning to generate scenery multiple times it is best to pre-process the files yourself.

The easiest way to do these operations is using QGIS, which is available for most platforms. If you are scripting a toolchain, the QGIS tools include command-line equivalents for all commands.

When using QGIS, set the Project CRS to WGS84 (aka EPSG:4326). You can then add layers of Raster or Vector data from files from the <code>Layer->Add Layer</code> menu. When performing any operations, <u>always</u> write out the data to a real file so you can go back to it later. Disk space is cheap :).

=== Warping Raster Layers to WGS84 ===
genVPB.py will automatically convert to WGS84 if it detects a landclass raster with a different Coordinate System. However, you may wish to do so manually instead so all your data is on the same coordinate system.

In QGIS you can warp a raster layer to a different CRS using the Raster->Projections-Warp (Reproject) tool.

Select the following options in the dialog:

* Input Layer - Check you have selected the correct layer. The CRS is shown at the right.
* Target CRS - set to <code>EPSG:4326 - WGS84</code>, which should be your project CRS.
* Resampling Method to Use - Nearest Neighbour. (Landclass data is not like normal images. You don't want to interpolate between values.)
* Nodata value for output bands - 0.0 (This means that any data at the edges will be Ocean, usually a reasonable default)
* Advanced Parameters - No Compression
* Output data type - Byte
* Reprojected - Choose a target filename

Alternatively you can do this step from the commandline.
gdalwarp -t_srs EPSG:4326 -dstnodata 0.0 -r near -ot Byte -of GTiff -co COMPRESS=NONE -co BIGTIFF=IF_NEEDED /home/stuart/FlightGear/VPB/data/CORINE/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif /home/stuart/FlightGear/VPB/data/scratch/corine_WGS84.tif

=== Warping Vector Layers to WGS84 ===
genVPB.py will automatically convert to WGS84 if it detects a landclass raster with a different Coordinate System. However, you may wish to do so manually instead so all your data is on the same coordinate system.

In QGIS you can warp a vector layer using Vector->Data Management Tools->Reproject Layer.

Set the following options in the dialog:

* Input Layer - Check you have selected the correct layer. The CRS is shown at the right.
* Target CRS - set to <code>EPSG:4326 - WGS84</code>, which should be your project CRS.
* Reprojected - Choose a target filename

=== Reclassifying Vector Layers ===
For CORINE vector data in particular, the attributes used in the vector data are not the same as those used by the CORINE Raster data. So we need to create a new attribute on the data.
[[File:Field Calculator.png|thumb|QGIS Field Calculator]]
To do this

* Select <code>Layer->Open Attribute Table</code>. You should see a table with multiple columns. Each row represents a feature in the data.
* Click on <code>Toggle Editing Mode</code> (Ctrl + E), on the top left of the dialog
* Click on <code>New Field</code> (Ctrl+W) and create a new field called Landclass of type "Whole Number (Integer)". This will create a new column which we will populate with the correct landclass data
* Click on the <code>Open Field Calculator</code> button (Ctrl + I). (If you get an error about only being able to create Virtual fields, go back to the Layer menu, export it and open the exported file).
* Select the following options:
** Update Existing Field
** Select the Landclass field you just created.
** Copy the contents of https://sourceforge.net/p/flightgear/fgmeta/ci/next/tree/ws30/mappings/corine_vector.txt into the Expression box (without the comment lines starting with <code>#</code>). This is just some simple code to set the attribute correctly. The code should be correct for CORINE vector data. If your data is from other sources you will need to work out how you want to map your source data landclasses to the CORINE ones. [https://sourceforge.net/p/flightgear/fgdata/ci/next/tree/Materials/base/landclass-mapping.xml Materials/base/landclass-mapping.xml] can be used as a guide.
* Select <code>OK</code>. You should see that your landclass column is now populated with the landclass data.
* Select <code>Layer->Save Layer Edits</code> to save you changes

=== Creating a Raster from a Vector Layer ===
To create a Raster from a Vector Layer select <code>Raster->Conversion->Rasterize (Vector to Raster)</code>.
[[File:QGIS Rasterize (Vector to Raster).png|thumb|Creating a Raster from a Vector Layer - QGIS Rasterize]]
Select the following options:

* Input Layer - correct layer, check CRS
* Field to use for burn-in value - select the Landclass column you created above.
* Output raster size units. This is going to set the resolution of your raster. You can work out the resolution in two different ways:
** Select "Georeferenced units" and determine how many degrees each pixel is in latitude and longitude.
** Select "Pixels" and determine the size of raster you want in pixels. [https://www.nhc.noaa.gov/gccalc.shtml This] is a good calculator to help.
* Width/Horizontal Resolution. Enter the values you've calculated for the horizontal resolution (longitudinal), or the width of the raster
* Height/Vertical Resolution. Enter the values you've calculated for the vertical resolution (latitude or the height of the raster
* Output extent - Select an option from the box on the right. You can edit the text afterwards. Best practise is to create long thin strips of 1 degree latitude in height, as this makes subsequent processing much easier.
* Assign a specific nodata value to output bands - Select 0.0 for Ocean. CORINE vector data in particular has a lot of nodata for Oceans
* Advanced Parameters - No Compression
* Output data type - Byte
* Rasterized - Select a new filename

=== Simplifying a Raster Layer ===
Some Raster Landclass data (NLCD included) has too much noise - in particular large US highway systems are identified as Urban areas.

To smooth it out we can use the GRASS <code>n.neighbors</code> function from the Processing Toolbox in QGIS.

Select the following options:

* Input Layer - correct layer, check CRS
* Neighborhood operation - median. (This is not a normal image, so using an average will result in weird values)
* Neighborhood size - 5.
* Neighbors - Select a new filename.

=== Reclassifying a Raster Layer ===
WS3.0 uses CORINE landclass values. If using data from other sources it needs to be reclassified to the correct values. genVPB.py has an option to do this, but you may wish to do so manually.

To do this select <code>GRASS->Raster->r.reclass</code> from the Processing Toolbox.

Select the following options:

* Input Raster Layer - correct layer, check CRS
* Reclass rules text - copy in the contents of https://sourceforge.net/p/flightgear/fgmeta/ci/next/tree/ws30/mappings/nlcd2019.txt. Or an appropriate mapping from your landclass data to CORINE. Note that you can also reference a file using the "File containing reclass rules" option. Note a mapping of 22 24 = 1 is the same as 22 and 24 = 1. For a range of 22 to 24 use 22 23 24 = 1.
* Reclassified - Select a new filename.

(If this doesn't work a similar function is available in the Processing Toolbox under <code>Raster analysis->Reclassify by table</code>. However this doesn't save your table once you close the dialog, and entries have to be manually entered individually which takes a lot of effort)

=== Step By Step Procedure for Processing NLCD for the USA using the Raster Calculator, Up sampling and GRASS r.neighbors===

We will use a predetermined file naming convention throughout this procedure for simplicity. You can use a naming convention of your choice.

Obtain your NLCD_2019_Land_Cover from source of choice, https://www.mrlc.gov/viewer/

NLCD_2019_Land_Cover data = NLCD_2019_Land_Cover_California-Southern.tiff

Obtain your NLCD_2016_Tree_Canopy from source of choice, https://www.mrlc.gov/viewer/

NLCD_2016_Tree_Canopy data = NLCD_2016_Tree_Canopy_California-Southern.tiff

Make Deciduous Layer 255 to 0 EPSG:5070 - NAD83 - Raster Calculator

("NLCD_2016_Tree_Canopy_California-Southern@1" = 255) * 0 + ("NLCD_2016_Tree_Canopy_California-Southern@1" != 255) * "NLCD_2016_Tree_Canopy_California-Southern@1"

Output CRS = EPSG:5070 - NAD83

Output Layer = California-Southern_deciduous-coast-clipped.tiff

OK

Make Tree Canopy Layer EPSG:5070 - NAD83 - Raster Calculator

Use the prevailing tree type, for Deciduous use * 41, for Evergreen use * 42, for MixedForest use 43.

Later we add this Tree Canopy layer to the raster where there is currently no trees in the NLCD

If ocean bordering with index 255 -

("California-Southern_deciduous-coast-clipped@1" != 0) * 42

Else

("NLCD_2016_Tree_Canopy_California-Southern@1" != 0) * 42

Output CRS = EPSG:5070 - NAD83

Output Layer = California-Southern_deciduous.tiff

OK

Make Clipped Ocean Frontage EPSG:5070 - NAD83 - GIMP

Source = NLCD_2019_Land_Cover_California-Southern.tiff

Flood fill index 11 (blue) with 0 (black)

Export tiff = California-Southern_coast-clipped.tiff

Warp NLCD Deciduous and Base NLCD from EPSG:5070 - NAD83 / Conus Albers NLCD to Project CRS: EPSG:4326 - WGS 84 - WARP

If ocean bordering with index 255

Warp California-Southern_coast-clipped.tiff

Else

Warp NLCD_2016_Tree_Canopy_California-Southern.tiff

and

Warp Deciduous NLCD from EPSG:5070 - NAD83 / Conus Albers NLCD to Project CRS: EPSG:4326 - WGS 84

Warp California-Southern_deciduous.tiff

Raster -> Projection - > Warp

Target CRS = EPSG:4326 - WGS 84

Resampling method to use = Nearest Neighbour

Output data type = byte

Reprojected = California-Southern_4326-84.tiff and California-Southern_deciduous_4326-84.tiff

Run

Combine "California-Southern_deciduous_4326-84.@1" and "California-Southern_4326-84@1" - Raster Calculator

This step adds the Tree Canopy layer to the existing NLCD raster only where there is currently no existing tree land cover.

(("California-Southern_4326-84@1" > 0 AND "California-Southern_4326-84@1" != 41 AND "California-Southern_4326-84@1" != 42 AND "California-Southern_4326-84@1" != 43) AND "California-Southern_deciduous_4326-84@1" > 0) * "California-Southern_deciduous_4326-84@1" + ("California-Southern_4326-84@1" = 41 OR "California-Southern_4326-84@1" = 42 OR "California-Southern_4326-84@1" = 43 OR "California-Southern_deciduous_4326-84@1" <= 0) * "California-Southern_4326-84@1"

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_adjusted.tiff

OK

Re-class urban 21, 22, 23, 24 = grass 26 - Raster Calculator

This step is being used to erase all the man made clutter on the raster with grassland. Instead of grassland you can replace it with any type of prevailing groundcover in the area your processing.

For example this places a road easement of grass on all the road clutter that was originally in the raster. If your processing a desert area you might want to use dirt or sand instead of grassland. For Arctic areas maybe use tundra.

Part of the clutter gets replaced later in the process.

("California-Southern_adjusted@1" = 11) * 41 + ("California-Southern_adjusted@1" = 12) * 34 + ("California-Southern_adjusted@1" = 21) * 26 + ("California-Southern_adjusted@1" = 22) * 26 + ("California-Southern_adjusted@1" = 23) * 26 + ("California-Southern_adjusted@1" = 24) * 26 + ("California-Southern_adjusted@1" = 31) * 27 + ("California-Southern_adjusted@1" = 41) * 26 + ("California-Southern_adjusted@1" = 42) * 24 + ("California-Southern_adjusted@1" = 43) * 25 + ("California-Southern_adjusted@1" = 51) * 30 + ("California-Southern_adjusted@1" = 52) * 29 + ("California-Southern_adjusted@1" = 71) * 26 + ("California-Southern_adjusted@1" = 72) * 32 + ("California-Southern_adjusted@1" = 73) * 31 + ("California-Southern_adjusted@1" = 74) * 31 + ("California-Southern_adjusted@1" = 75) * 32 + ("California-Southern_adjusted@1" = 81) * 18 + ("California-Southern_adjusted@1" = 82) * 19 + ("California-Southern_adjusted@1" = 90) * 25 + ("California-Southern_adjusted@1" = 95) * 35

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_reclass-grass.tiff

OK

Make Urban layer

("California-Southern_adjusted@1" = 21) * 10 + ("California-Southern_adjusted@1" = 22) * 1 + ("California-Southern_adjusted@1" = 23) * 1 + ("California-Southern_adjusted@1" = 24) * 2

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_urban.tiff

OK

Remove the road clutter with r.neighbor

GRASS/Raster/r.neighbor

Neighborhood operation = median

Neighborhood size = 7

Neighbors = California-Southern_urban-only.tiff

Run

Combine "California-Southern_reclass-urban@1" and "California-Southern_reclass-grass@1"

This step replaces the "cleaned up" urban areas back into the raster.

("California-Southern_urban-only@1" < 1) * "California-Southern_reclass-grass@1" + ("California-Southern_urban-only@1" != 0) * "California-Southern_urban-only@1"

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_adjusted-combined.tiff

OK

Up sample California-Southern_adjusted-combined.tiff 2x, 4x or 8x resolution 0.00008309125

Active Layer = California-Southern_adjusted-combined.tiff

Layer/Save As

2x Horizontal = 0.00008309125

2x Vertical = 0.00008309125

4x Horizontal = 0.00010637625

4x Vertical = 0.00010637625

8x Horizontal = 0.00005318812

8x Vertical = 0.00005318812

File Name = California-Southern_final-prep-4x.tiff

OK

Simplify California-Southern_4326-84-hd.tiff with r.neighbor

GRASS/Raster/r.neighbor

Neighborhood operation = median

Neighborhood size = 7

Neighbors = California-Southern_urban-only.tiff

Run

Reclass index 0 to 44, leave the rest the same

("California-Southern_4326-84-hd@1" = 0) * 44 + ("California-Southern_4326-84-hd@1" != 0) * "California-Southern_4326-84-hd@1"

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_4326-84-hd-corrected.tiff

OK

<big>Optional HD Fresh Water Option</big>
*Step 1 - Obtain and load hi resolution vector layer
Make sure that the vector layer and the raster layer you will eventually merge to have the same projection. ** Extent can be different if you use the option below.
#Use Top Menu: "Raster" -> "Conversion" -> "Rasterize (vector to raster)" or Processing Toolbox: GDAL -> "Vector conversion" -> "Rasterize (vector to raster)"
# Input layer = Southern-California_water_4326-84
#Fixed value to burn = 41 (water)
# Output raster size units = "Georeferenced units"
#Width/Horizontal resolution = 0.00008309125 (4x the base NLCD resolution )
#Height/Vertical resolution = 0.00008309125 (4x the base NLCD resolution)
#<nowiki>** Output extent = Select the raster layer you will eventually merge with as the "Output extent".</nowiki>
#Under "Rasterized" choose a new filename to save the new raster to. We'll use Southern-California_4326-84-hd-water.tiff.

Click "Run" to generate the hi resolution raster layer.

*Step 2 - Reclass hi res, smoothed water to grass
# Open Raster Calculator
#Under "Output layer" choose a new filename to save the new raster to. We'll use Southern-California_4326-84-hd-nowater.tiff.
#Copy the following into the "Raster Calculator Expression" box. Where Southern-California_4326-84-hd is the name of your hi resolution, smoothed NLCD that includes the water data.
"Southern-California_4326-84-hd@1" * ("Southern-California_4326-84-hd@1" != 41) + 26 * ("Southern-California_4326-84-hd@1 = 41")

Click on "OK". When finished you will have a new raster with the water layer changed to grassland. Another choice would be to change it to sand.

*Step 3 - Combine the hi resolution no water raster and the hi resolution water raster.
# Use Top Menu: "Raster" -> "Miscellaneous" -> "Merge" or Processing Toolbax: GDAL -> Raster Miscellaneous -> Merge
#Input layers = Select "Southern-California_4326-84-hd-nowater@1" and Southern-California_4326-84-hd-water.tiff
#Output data type = "byte".
# Under "Merged" choose a new filename to save the new raster to. We'll use Southern-California_4326-84-hdwater.tiff.

Click "Run" to generate the new merged hi resolution raster layer.

===Generating the Terrain using osgdem===
Instead of using genVPB.py, you may wish to run osgdem directly.

In the Windows/Docker platform you can send the generate tile command directly to osgdem.exe, one tile at a time.

Using the NLCD raster processing convention from above, following is the the final step after creating the raster and entering bash shell with the windows version of "docker run..."

osgdem --TERRAIN --image-ext png --RGBA --no-interpolate-imagery --disable-error-diffusion --geocentric --no-mip-mapping -t ./data/California-Southern_4326-84-hd-corrected.tiff -d ./SRTM-3/N32W115.hgt -b -115 32 -114 33 --PagedLOD -l 7 --radius-to-max-visible-distance-ratio 3 -o ./output/vpb/w120n30/w115n32/ws_w115n32.osgb

Note: the --image-ext png --RGBA flags are critical to successfully building correctly placed landclasses in the final VPB generated scenery.

If you prefer to run the scenery generation manually, running the VPB osgdem process is described in more detail here: [[Virtual Planet Builder#Running VPB]].

After doing this you should have an output directory containing files of the form <code>output/vpb/w010n50/w004n50/ws_w004n50.osgb</code>, plus a host of sub-directories. Each one of these is a 1x1 tile of terrain.

to leave the container simply type <code>exit</code>.

=== Packaging the Scenery===
Once you have the terrain and line features they should be packaged in a scenery directory in vpb and Terrain sub-directories respectively. E.g.
MyCoolScenery/Terrain
MyCoolScenery/vpb
It is good practise to document the data sources used in scenery generation. Some source licenses require attribution of the original data source for anything derived, published or distributed.

To assist in fulfilling these license obligations, you can create a source.xml file in the scenery directory which includes attribution information. This will then be available from within the simulator under Help->Scenery Sources, and <u>may</u> fulfil the attribution requirements of your license. '''Note that you are responsible for fulfilling any license requirements from the data, not FlightGear'''.

The format of the file is straightforward:
<?xml version="1.0"?>
<PropertyList>
<nowiki><source></nowiki>
<name>Corine Land Cover (CLC) 2018, Version 2020_20u1</name>
<nowiki><link></nowiki><nowiki>http://web.archive.org/web/20221112175615/https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata%2A</nowiki><nowiki></link></nowiki>
<license>GMES Open License</license>
<nowiki></source></nowiki>
<nowiki><source></nowiki>
<name>NASADEM Merged DEM Global 1 arc second V001</name>
<nowiki><link></nowiki><nowiki>https://www.earthdata.nasa.gov/</nowiki><nowiki></link></nowiki>
<license>Public Domain</license>
<nowiki></source></nowiki>
<nowiki><source></nowiki>
<name>OpenStreetMap</name>
<nowiki><link></nowiki><nowiki>https://www.openstreetmap.org/copyright</nowiki><nowiki></link></nowiki>
<license>Open Data Commons Open Database License</license>
<nowiki></source></nowiki>
</PropertyList>

Ubuntu 24.04 Package List and Build Hints

2024-06-07T16:01:35Z

Stuart:

Packages required to build openscenegraph, simgear and flightgear next on Ubuntu 24.04 LTS.

<code>sudo apt-get install g++ build-essential libpng12-dev libgl1-mesa-dev libnvtt-dev libboost-dev libopenal-dev libcurl3-dev qtbase5-private-dev qtdeclarative5-private-dev libplib-dev qt5 qttools5-dev libqt5svg5-dev qml-module-qtquick-controls2</code>

2023-12-21T16:31:57Z

Stuart:

{{WS30 Navbar}}
This article provides instructions on how to generate base WS3.0 terrain.

WS3.0 terrain consists of two parts:

# A landclass texture draped over an elevation model. This is the terrain mesh.
# Line features such as roads, railways, rivers and coastline. This is draped over the terrain mesh at runtime.
[[File:Diagram-export-21-12-2023-16 29 37.png|thumb|Basic WS3.0 Scenery Generation Process]]

== Getting the base data ==
First, you need two pieces of data for the scenery area you are generating:

# An elevation model (aka DEM). Use of the "NASADEM" with 30m resolution is recommended, and is available from [https://lpdaac.usgs.gov/products/nasadem_hgtv001/ here], and with an interactive browser [https://search.earthdata.nasa.gov/search here].
# Landclass data showing what type of terrain is at each point of the surface. This is often either a Raster Geotiff (effectively a texture), or vector data.
#* For Europe, use of [https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 CORINE] is recommended.
#* For the USA NLCD is recommended
#* Sentinel-2 data is available for the entire world via [https://livingatlas.arcgis.com/landcoverexplorer/ ESRI], but has limited set of landclasses.
Put these files into a <code>data</code> directory and create an <code>output</code> directory next to it. This is where the scenery will be generated..

== Pre-processing the Elevation Model ==
If you are using NASADEM, then the elevation model is already using WGS84 and is ready to use as-is.

If you are using another elevation model, then you may need to re-project it using QGIS/gdalwarp to the WGS84 CRS (aka EPSG:4326).

== Pre-processing the Landclass Data ==
For any landclass data we need to ensure the data is in the correct format. That means:

# Is a Raster (geotiff) rather than Vector data. This raster will become the texture on the terrain that the terrain shaders do their magic on.
# Uses the WGS84 Coordinate Reference System. The ensures that the terrain generation step is efficient.
# Has the correct landclass values for each terrain type. Different landclass systems use different values. At present we use the CORINE values, defined in [https://sourceforge.net/p/flightgear/fgdata/ci/next/tree/Materials/base/landclass-mapping.xml Materials/base/landclass-mapping.xml].

Below is a quick table showing what steps you need to take for common landclass data sources.
{| class="wikitable"
|+
!Landclass Data
!Warp to WGS84 required?
!Landclass re-classification Required?
!Raster Simplification Required?
!Conversion to Raster Required?
|-
|CORINE Raster
|Yes
|No
|No
|No
|-
|CORINE Vector
|Yes
|Yes
|No
|Yes
|-
|NLCD
|No
|Yes
|Yes
|No
|-
|Sentinel-2
|Yes
|Yes
|No
|No
|}
Conversion to Raster must be done manually. Converting to WGS82 and the correct landclasses ''can'' be done by the genVPB.py script, but slows down scenery generation. Therefore if you are planning to generate scenery multiple times it is best to pre-process the files yourself.

The easiest way to do these operations is using QGIS, which is available for most platforms. If you are scripting a toolchain, the QGIS tools include command-line equivalents for all commands.

When using QGIS, set the Project CRS to WGS84 (aka EPSG:4326). You can then add layers of Raster or Vector data from files from the <code>Layer->Add Layer</code> menu. When performing any operations, <u>always</u> write out the data to a real file so you can go back to it later. Disk space is cheap :).

=== Warping Raster Layers to WGS84 ===
This is applicable to CORINE Raster data

You can warp a raster layer to a different CRS using the Raster->Projections-Warp (Reproject) tool.

Select the following options in the dialog:

* Input Layer - Check you have selected the correct layer. The CRS is shown at the right.
* Target CRS - set to <code>EPSG:4326 - WGS84</code>, which should be your project CRS.
* Resampling Method to Use - Nearest Neighbour. (Landclass data is not like normal images. You don't want to interpolate between values.)
* Nodata value for output bands - 0.0 (This means that any data at the edges will be Ocean, usually a reasonable default)
* Advanced Parameters - No Compression
* Output data type - Byte
* Reprojected - Choose a target filename

Alternatively you can do this step from the commandline.
gdalwarp -t_srs EPSG:4326 -dstnodata 0.0 -r near -ot Byte -of GTiff -co COMPRESS=NONE -co BIGTIFF=IF_NEEDED /home/stuart/FlightGear/VPB/data/CORINE/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif /home/stuart/FlightGear/VPB/data/scratch/corine_WGS84.tif

=== Warping Vector Layers to WGS84 ===
You can warp a vector layer using Vector->Data Management Tools->Reproject Layer.

Set the following options in the dialog:

* Input Layer - Check you have selected the correct layer. The CRS is shown at the right.
* Target CRS - set to <code>EPSG:4326 - WGS84</code>, which should be your project CRS.
* Reprojected - Choose a target filename

=== Reclassifying Vector Layers ===
For CORINE vector data in particular, the attributes used in the vector data are not the same as those used by the CORINE Raster data. So we need to create a new attribute on the data.
[[File:Field Calculator.png|thumb|QGIS Field Calculator]]
To do this

* Select <code>Layer->Open Attribute Table</code>. You should see a table with multiple columns. Each row represents a feature in the data.
* Click on <code>New Field</code> (Ctrl+W) and create a new field called Landclass of type "Whole Number (Integer)". This will create a new column which we will populate with the correct landclass data
* Click on the <code>Open Field Calculator</code> button (Ctrl + I). (If you get an error about only being able to create Virtual fields, go back to the Layer menu, export it and open the exported file).
* Select the following options:
** Update Existing Field
** Select the Landclass field you just created.
** Copy the contents of https://sourceforge.net/p/flightgear/fgmeta/ci/next/tree/ws30/mappings/corine_vector.txt into the Expression box. This is just some simple code to set the attribute correctly. The code should be correct for CORINE vector data. If your data is from other sources you will need to work out how you want to map your source data landclasses to the CORINE ones. [https://sourceforge.net/p/flightgear/fgdata/ci/next/tree/Materials/base/landclass-mapping.xml Materials/base/landclass-mapping.xml] can be used as a guide.
* Select <code>OK</code>. You should see that your landclass column is now populated with the landclass data.
* Select <code>Layer->Save Layer Edits</code> to save you changes

=== Creating a Raster from a Vector Layer ===
To create a Raster from a Vector Layer select <code>Raster->Conversion->Rasterize (Vector to Raster)</code>.
[[File:QGIS Rasterize (Vector to Raster).png|thumb|Creating a Raster from a Vector Layer - QGIS Rasterize]]
Select the following options:

* Input Layer - correct layer, check CRS
* Field to use for burn-in value - select the Landclass column you created above.
* Output raster size units. This is going to set the resolution of your raster. You can work out the resolution in two different ways:
** Select "Georeferenced units" and determine how many degrees each pixel is in latitude and longitude.
** Select "Pixels" and determine the size of raster you want in pixels. [https://www.nhc.noaa.gov/gccalc.shtml This] is a good calculator to help.
* Width/Horizontal Resolution. Enter the values you've calculated for the horizontal resolution (longitudinal), or the width of the raster
* Height/Vertical Resolution. Enter the values you've calculated for the vertical resolution (latitude or the height of the raster
* Output extent - Select an option from the box on the right. You can edit the text afterwards.
* Assign a specific nodata value to output bands - Select 0.0 for Ocean. CORINE vector data in particular has a lot of nodata for Oceans
* Advanced Parameters - No Compression
* Output data type - Byte
* Rasterized - Select a new filename

=== Simplifying a Raster Layer ===
Some Raster Landclass data (NLCD included) has too much noise - in particular large US highway systems are identified as Urban areas.

To smooth it out we can use the GRASS <code>n.neighbors</code> function from the Processing Toolbox in QGIS.

Select the following options:

* Input Layer - correct layer, check CRS
* Neighborhood operation - median. (This is not a normal image, so using an average will result in weird values)
* Neighborhood size - 5.
* Neighbors - Select a new filename.

=== Reclassifying a Raster Layer ===
WS3.0 uses CORINE landclass values. If using data from other sources it needs to be reclassified to the correct values.

To do this select <code>GRASS->Raster->r.reclass</code> from the Processing Toolbox.

Select the following options:

* Input Raster Layer - correct layer, check CRS
* Reclass rules text - copy in the contents of https://sourceforge.net/p/flightgear/fgmeta/ci/next/tree/ws30/mappings/nlcd2019.txt. Or an appropriate mapping from your landclass data to CORINE. Note that you can also reference a file using the "File containing reclass rules" option. Note a mapping of 22 24 = 1 is the same as 22 and 24 = 1. For a range of 22 to 24 use 22 23 24 = 1.
* Reclassified - Select a new filename.

(If this doesn't work a similar function is available in the Processing Toolbox under <code>Raster analysis->Reclassify by table</code>. However this doesn't save your table once you close the dialog, and entries have to be manually entered individually which takes a lot of effort)

=== Step By Step Procedure for Processing NLCD for the USA using the Raster Calculator, Up sampling and GRASS r.neighbors===

We will use a predetermined file naming convention throughout this procedure for simplicity. You can use a naming convention of your choice.

Obtain your NLCD_2019_Land_Cover from source of choice, https://www.mrlc.gov/viewer/

NLCD_2019_Land_Cover data = NLCD_2019_Land_Cover_California-Southern.tiff

Obtain your NLCD_2016_Tree_Canopy from source of choice, https://www.mrlc.gov/viewer/

NLCD_2016_Tree_Canopy data = NLCD_2016_Tree_Canopy_California-Southern.tiff

Make Deciduous Layer 255 to 0 EPSG:5070 - NAD83 - Raster Calculator

("NLCD_2016_Tree_Canopy_California-Southern@1" = 255) * 0 + ("NLCD_2016_Tree_Canopy_California-Southern@1" != 255) * "NLCD_2016_Tree_Canopy_California-Southern@1"

Output CRS = EPSG:5070 - NAD83

Output Layer = California-Southern_deciduous-coast-clipped.tiff

OK

Make Tree Canopy Layer EPSG:5070 - NAD83 - Raster Calculator

If ocean bordering with index 255 - For Deciduous use * 41, for Evergreen use * 42, for Mixed Forest use 43

Some areas may require the Tree Canopy Layer to be re-classified to multiple tree tree types.

("California-Southern_deciduous-coast-clipped@1" != 0) * 42

Else

("NLCD_2016_Tree_Canopy_California-Southern@1" != 0) * 42

Output CRS = EPSG:5070 - NAD83

Output Layer = California-Southern_deciduous.tiff

OK

Make Clipped Ocean Frontage EPSG:5070 - NAD83 - GIMP

Source = NLCD_2019_Land_Cover_California-Southern.tiff

Flood fill index 11 (blue) with 0 (black)

Export tiff = California-Southern_coast-clipped.tiff

Warp NLCD Deciduous and Base NLCD from EPSG:5070 - NAD83 / Conus Albers NLCD to Project CRS: EPSG:4326 - WGS 84 - WARP

If ocean bordering with index 255

Warp California-Southern_coast-clipped.tiff

Else

Warp NLCD_2016_Tree_Canopy_California-Southern.tiff

and

Warp Deciduous NLCD from EPSG:5070 - NAD83 / Conus Albers NLCD to Project CRS: EPSG:4326 - WGS 84

Warp California-Southern_deciduous.tiff

Raster -> Projection - > Warp

Target CRS = EPSG:4326 - WGS 84

Resampling method to use = Nearest Neighbour

Output data type = byte

Reprojected = California-Southern_4326-84.tiff and California-Southern_deciduous_4326-84.tiff

Run

Combine "California-Southern_deciduous_4326-84.@1" and "California-Southern_4326-84@1" - Raster Calculator

("California-Southern_deciduous_4326-84@1" < 1) * "California-Southern_4326-84@1" + ("California-Southern_deciduous_4326-84@1" != 0) * "California-Southern_deciduous_4326-84@1"

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_adjusted.tiff

OK

Re-class urban 21, 22, 23, 24 = grass 26 - Raster Calculator

This step is being used to erase all the man made clutter on the raster with grassland. Instead of grassland you can replace it with any type of prevailing groundcover in the area your processing.

For example this places a road easement of grass on all the road clutter that was originally in the raster. If your processing a desert area you might want to use dirt or sand instead of grassland. For Arctic areas maybe use tundra.

Part of the clutter gets replaced later in the process.

("California-Southern_adjusted@1" = 11) * 41 + ("California-Southern_adjusted@1" = 12) * 34 + ("California-Southern_adjusted@1" = 21) * 26 + ("California-Southern_adjusted@1" = 22) * 26 + ("California-Southern_adjusted@1" = 23) * 26 + ("California-Southern_adjusted@1" = 24) * 26 + ("California-Southern_adjusted@1" = 31) * 27 + ("California-Southern_adjusted@1" = 41) * 26 + ("California-Southern_adjusted@1" = 42) * 24 + ("California-Southern_adjusted@1" = 43) * 25 + ("California-Southern_adjusted@1" = 51) * 30 + ("California-Southern_adjusted@1" = 52) * 29 + ("California-Southern_adjusted@1" = 71) * 26 + ("California-Southern_adjusted@1" = 72) * 32 + ("California-Southern_adjusted@1" = 73) * 31 + ("California-Southern_adjusted@1" = 74) * 31 + ("California-Southern_adjusted@1" = 75) * 32 + ("California-Southern_adjusted@1" = 81) * 18 + ("California-Southern_adjusted@1" = 82) * 19 + ("California-Southern_adjusted@1" = 90) * 25 + ("California-Southern_adjusted@1" = 95) * 35

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_reclass-grass.tiff

OK

Make Urban layer

("California-Southern_adjusted@1" = 21) * 10 + ("California-Southern_adjusted@1" = 22) * 1 + ("California-Southern_adjusted@1" = 23) * 1 + ("California-Southern_adjusted@1" = 24) * 2

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_urban.tiff

OK

Remove the road clutter with r.neighbor

GRASS/Raster/r.neighbor

Neighborhood operation = median

Neighborhood size = 7

Neighbors = California-Southern_urban-only.tiff

Run

Combine "California-Southern_reclass-urban@1" and "California-Southern_reclass-grass@1"

This step replaces the "cleaned up" urban areas back into the raster.

("California-Southern_urban-only@1" < 1) * "California-Southern_reclass-grass@1" + ("California-Southern_urban-only@1" != 0) * "California-Southern_urban-only@1"

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_adjusted-combined.tiff

OK

Up sample California-Southern_adjusted-combined.tiff 4x resolution 0.00008309125

Active Layer = California-Southern_adjusted-combined.tiff

Layer/Save As

Horizontal = 0.00008309125

Vertical = 0.00008309125

File Name = California-Southern_final-prep-4x.tiff

OK

Simplify California-Southern_4326-84-hd.tiff with r.neighbor

GRASS/Raster/r.neighbor

Neighborhood operation = median

Neighborhood size = 7

Neighbors = California-Southern_urban-only.tiff

Run

Reclass index 0 to 44, leave the rest the same

("California-Southern_4326-84-hd@1" = 0) * 44 + ("California-Southern_4326-84-hd@1" != 0) * "California-Southern_4326-84-hd@1"

Output CRS = EPSG:4326 - WGS 84

Output Layer = California-Southern_4326-84-hd-corrected.tiff

OK

<big>Optional HD Fresh Water Option</big>
*Step 1 - Obtain and load hi resolution vector layer
Make sure that the vector layer and the raster layer you will eventually merge to have the same projection. ** Extent can be different if you use the option below.
#Use Top Menu: "Raster" -> "Conversion" -> "Rasterize (vector to raster)" or Processing Toolbox: GDAL -> "Vector conversion" -> "Rasterize (vector to raster)"
# Input layer = Southern-California_water_4326-84
#Fixed value to burn = 41 (water)
# Output raster size units = "Georeferenced units"
#Width/Horizontal resolution = 0.00008309125 (4x the base NLCD resolution )
#Height/Vertical resolution = 0.00008309125 (4x the base NLCD resolution)
#<nowiki>** Output extent = Select the raster layer you will eventually merge with as the "Output extent".</nowiki>
#Under "Rasterized" choose a new filename to save the new raster to. We'll use Southern-California_4326-84-hd-water.tiff.

Click "Run" to generate the hi resolution raster layer.

*Step 2 - Reclass hi res, smoothed water to grass
# Open Raster Calculator
#Under "Output layer" choose a new filename to save the new raster to. We'll use Southern-California_4326-84-hd-nowater.tiff.
#Copy the following into the "Raster Calculator Expression" box. Where Southern-California_4326-84-hd is the name of your hi resolution, smoothed NLCD that includes the water data.
"Southern-California_4326-84-hd@1" * ("Southern-California_4326-84-hd@1" != 41) + 26 * ("Southern-California_4326-84-hd@1 = 41")

Click on "OK". When finished you will have a new raster with the water layer changed to grassland. Another choice would be to change it to sand.

*Step 3 - Combine the hi resolution no water raster and the hi resolution water raster.
# Use Top Menu: "Raster" -> "Miscellaneous" -> "Merge" or Processing Toolbax: GDAL -> Raster Miscellaneous -> Merge
#Input layers = Select "Southern-California_4326-84-hd-nowater@1" and Southern-California_4326-84-hd-water.tiff
#Output data type = "byte".
# Under "Merged" choose a new filename to save the new raster to. We'll use Southern-California_4326-84-hdwater.tiff.

Click "Run" to generate the new merged hi resolution raster layer.

==Setting up Virtual Planet Builder using docker==
Generating the scenery uses an OSG tool called Virtual Planet Builder. Fortunately, there is a docker image so you don't need to build this yourself. Instead it will run as a container inside docker.

To set this up:

#Install [https://docs.docker.com/get-started/ Docker] on your platform.
#On your own machine, pull the docker image
docker pull flightgear/ws30-vpb-generator:latest

You now have a docker image on your own machine you can run.

Optionally, if you are hitting rate limits:
#Create an account on https://hub.docker.com/. (Note that you will need to click on an email verification link before you can log in for the first time)
#Run the following before the '''docker pull''' command above
docker login

==Running the Container==
To generate terrain you need to run the Virtual Planet Builder tool within the container.

Firstly, get the container running from the directory containing your <code>data</code> and <code>output</code> directories:
docker run --rm --mount "type=bind,source=`pwd`/data,target=/home/flightgear/data,readonly" --mount "type=bind,source=`pwd`/output,target=/home/flightgear/output" -it flightgear/ws30-vpb-generator:latest /bin/bash

In a Windows environment the following can be used to build in the folder structure of

data

output/vbp
docker run --rm --mount "type=bind,source=`pwd`/data,target=/home/flightgear/data,readonly" --mount "type=bind,source=`pwd`/SRTM-3,target=/home/flightgear/SRTM-3,readonly" --mount "type=bind,source=`pwd`/output,target=/home/flightgear/output" --mount "type=bind,source=`pwd`/output/vpb,target=/home/flightgear/output/vpb" -it flightgear/ws30-vbp-generator:latest /bin/bash

You should now find yourself in a bash shell within your container. You should see data and output directories which are linked to the directories you created earlier:
flightgear@ddcac77f7d5e:~$ ls
data output

==Generating the Terrain==
The genVPB.py script will generate tiles of terrain from a landclass raster:
Usage: genVPB.py <min-lon> <min-lat> <max-lon> <max-lat> <input-raster> [--reclass <reclass>] [--coastline <coastline>] [--hgt-dir <hgt-dir>] [--output-dir <output-dir>]
<min-lon> <min-lat> <max-lon> <max-lat> - bounding box of scenery to generated
<input-raster> - Input landclass raster
[coastline] - Optional coastline polygon data (.osm) to clip against. E.g. from <nowiki>https://osmdata.openstreetmap.de/data/land-polygons.html</nowiki>
[reclass] - Optional file containing a set of rules for reclassifying the raster similar to r.reclass. See fgmeta/ws30/mappings/
[hgt-dir] - Optional directory containing unzipped NASADEM HGT files. Defaults to '/home/flightgear/data'.
[output-dir] - Optional directory for output scenery. Defaults to '/home/flightgear/output/vpb'

In the Windows/Docker platform you can send the generate tile command directly to osgdem.exe, one tile at a time.

Using the NLCD raster processing convention from above, following is the the final step after creating the raster and entering bash shell with the windows version of "docker run..."

osgdem --TERRAIN --image-ext png --RGBA --no-interpolate-imagery --disable-error-diffusion --geocentric --no-mip-mapping -t ./data/California-Southern_4326-84-hd-corrected.tiff -d ./SRTM-3/N32W115.hgt -b -115 32 -114 33 --PagedLOD -l 7 --radius-to-max-visible-distance-ratio 3 -o ./output/vpb/w120n30/w115n32/ws_w115n32.osgb

Note: the --image-ext png --RGBA flags are critical to successfully building correctly placed landclasses in the final VPB generated scenery.

If you prefer to run the scenery generation manually, running the VPB osgdem process is described in more detail here: [[Virtual Planet Builder#Running VPB]].

After doing this you should have an output directory containing files of the form <code>output/vpb/w010n50/w004n50/ws_w004n50.osgb</code>, plus a host of sub-directories. Each one of these is a 1x1 tile of terrain.

to leave the container simply type <code>exit</code>.

==Creating line features==
Line features such as roads, railways and rivers are simply stored as a series of lat/lon points in an STG file using a specific STG verb. We get the data of this from Openstreetmap, and we have some simple scripts to generate the files.

The scripts are already included in the docker image at <code>/home/flightgear/scripts/</code>.
===Running the scripts===
The genroads.py script generates all line features. Simply set the output directory and the lat/lon box. Note that the output directory should be a "Terrain" directory. E.g.
./scripts/genroads.py ./output/Terrain -4 55 -3 56

== Packaging the Scenery==
Once you have the terrain and line features they should be packaged in a scenery directory in vpb and Terrain sub-directories respectively. E.g.
MyCoolScenery/Terrain
MyCoolScenery/vpb
It is good practise to document the data sources used in scenery generation. Some source licenses require attribution of the original data source for anything derived, published or distributed.

To assist in fulfilling these license obligations, you can create a source.xml file in the scenery directory which includes attribution information. This will then be available from within the simulator under Help->Scenery Sources, and <u>may</u> fulfil the attribution requirements of your license. '''Note that you are responsible for fulfilling any license requirements from the data, not FlightGear'''.

The format of the file is straightforward:
<?xml version="1.0"?>
<PropertyList>
<nowiki><source></nowiki>
<name>Corine Land Cover (CLC) 2018, Version 2020_20u1</name>
<nowiki><link></nowiki><nowiki>http://web.archive.org/web/20221112175615/https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=metadata%2A</nowiki><nowiki></link></nowiki>
<license>GMES Open License</license>
<nowiki></source></nowiki>
<nowiki><source></nowiki>
<name>NASADEM Merged DEM Global 1 arc second V001</name>
<nowiki><link></nowiki><nowiki>https://www.earthdata.nasa.gov/</nowiki><nowiki></link></nowiki>
<license>Public Domain</license>
<nowiki></source></nowiki>
<nowiki><source></nowiki>
<name>OpenStreetMap</name>
<nowiki><link></nowiki><nowiki>https://www.openstreetmap.org/copyright</nowiki><nowiki></link></nowiki>
<license>Open Data Commons Open Database License</license>
<nowiki></source></nowiki>
</PropertyList>

==Running FlightGear with the new WS3.0 Terrain==
To test the new terrain, simply include the appropriate scenery directory (e.g. MyCoolScenery) in in your scenery path and run FlightGear with the <code>--prop:/scenery/use-vpb=true</code> to enable WS3.0.