Semi-Automated Building Footprint Extraction from Satellite Imagery for Autogen Creation in FSX/P3D


Resource contributor
Resource download available here:

Excuse the scientific research paper headline, but that's all I've been reading over the last few weeks! :D:D

Below I'll attempt to detail a method I have revised to semi-automatically produce autogen for FSX/P3D. Hopefully this will lead to some discussion and improvement that this entire community can benefit from. My results have been somewhat satisfactory using 2 different methods; one using free tools and the other using commercial (and rather expensive) software. Here I will outline the use of the free tools, and later, possibly produce a detailed document of every step necessary, when the results have been proven repeatable and reliable by others here.

Please note this method outlines the bare minimums to obtain barely useful results. This simplified approach is open to improvement. I am sure there are steps that I have left out that could improve results.

Tools needed:
· Quantum GIS (which comes with GRASS plugin):


· Your favorite GIS software: I prefer GlobalMapper but ArcGIS and QGIS can also be used.
· Your favorite image processing software like Photoshop, Paintshop or (free) gimp.
· And last but not least the hand-dandy ScenProc by Arno.


1. Obtain satellite imagery of your desired area. Obviously. :D
My test area was the Southwest quarter of Barbados, west of TBPB. Nice mix of densely packed small buildings (the hardest to create autogen for), small roads, bare earth, water and vegetation. It is helpful to have your image as a geo-rectified GeoTiff.

2. Start QuantumGIS Desktop with GRASS.
a. In the Plugins Menu go to GRASS > New Mapset.
b. Create a new GRASS database directory called “GRASSDATA” in our working directory of choice.
c. Create a new location and name it, example “Barbados”.

d. Create a new mapset, example “TBPB”.
e. Click Finish. The GRASS Tools on the right of QGIS should now become available for use.

3. Import your imagery into GRASS.

a. Click on the GRASS shell icon in your GRASS tools.
b. Type in to open the import window.
c. Browse to your image file and give the raster map a name, example “TBPB”. Click Run.

4. Start QuantumGIS Browser with GRASS.

The browser is a useful tool that enables you to easily access all the outputs we will be producing in QGIS, so that we can easily drag and drop them back into QGIS Desktop to view them again.

a. Navigate to your working directory, example “\GRASSDATA\Barbados\TBPB”. You should see a GRASS icon which represents your mapset, underneath which you should find blue, green and red bands of the imagery you imported in the previous step.

b. You may now choose to drag and drop any or all of these imagery bands into QGIS desktop to view them, but for the sake of a speedy work space, this is not yet necessary.

5. Set the region in QGIS.

a. In the GRASS shell, type g.region.
b. Under
Set region to match raster map(s) select any one of the imagery bands from the dropdown list and click run.
This sets the working area to the coordinates of our imagery. (Hence why having a Geotiff is helpful :) ). If you don’t have a Geotiff the coordinates can be set in the bounds tab.

6. Create an image group and subgroup

a. In the GRASS shell, type
b. In the Required tab, give the group a name, example “TBPB_group”.
c. In the Maps tab, select each band from the drop down list in turn, to add them to the group.
d. In the Optional tab, give the subgroup a name, example “TBPB_subgroup”.
e. Click run.

7. Perform an Unsupervised Classification.

a. In the GRASS shell, type i.cluster.
b. In the Required tab, select the group and subgroup.
c. Give the output for the signatures a name, example “TBPB_sig_11”.
d. In the Settings tab, set the initial number of classes to 11*.
*The number of classes you require for a particular image may be a matter of trial and error. The desired end result is an image where a particular range of values represent just roofs; we will discard all other values. It may be useful to create multiple signatures with different values and choose the best, example “TBPB_sig_9”, “TBPB_sig_17”, etc.
e. In the GRASS shell, type
f. In the Required tab, select the group, subgroup and signature.
g. Give the raster map a name, example “TBPB_uclass_11”.
h. Click Run.

i. Grab some milk and cookies. This might take a while.
j. Repeat with other signatures if necessary.

The QGIS Browser will now come in handy. Drag and drop each classified image to QGIS Browser for viewing and comparison.

k. Check what classes represent building footprints using the Identify Features Tool. Change the View to Table. Click on an area of the classified maps to take note of what values have been applied to roofs.
For example, with 11 classes, roof values typically range from 6 to 10.
Compare different classified images to see which gives the best range of values for building footprints.

8. Reclassify the chosen raster image.

a. In the GRASS shell, type r.reclass.
b. Select your chosen classified raster map to reclassify.
c. Give the output a name, example “TBPB_reclass_11”.
d. Enter rules for the reclassification.

0 thru 5 = 0

6 thru 10 = 255
11 = 0
What this does is changes all parts of the image which do not have the values which represent building footprints to black, and those that do are changed to white. These values will of course vary according to your image.

e. Click Run

We will now output the reclassified image to a BMP for post processing.

f. In the GRASS shell, type r.out.gdal.

g. Select the reclassified image.
h. Browse to a folder to save our output bitmap and give it a name, example “TBPB_reclass11.bmp”.
i. Change the raster data format to
BMP. This is the necessary format for the BREC tool.
j. In the Creation Tab, check Do not write the GDAL standard colortable and select Byte as the Data Type.
k. Click Run.
l. Use the QGIS browser to load the reclassified BMP image into QGIS Desktop. It should look similar to the following.

It will now be useful to remove white areas that you know are certainly not building footprints from the BMP. For example, you will notice large white areas in the bottom left corner of the above image and a few other irregularly shaped blobs, as well as most of the shoreline. This can be easily and quickly removed in your image processor of choice. I should quickly note that for production quality autogen, it will be useful to run classification on fully cleaned images which are absent of clouds and water. Use your water mask to turn all bodies of water (and especially waves which appear white) to black.

9. Use the Blob Remover Tool to clean up small specs.

a. Use CMD to open the Blob Remover tool. I found it useful to create a .bat file in notepad using similar code:
START cmd.exe /k "D:\Program Files\BREC\BlobRemover\blob_removerwin.exe"
This way I can open it faster and use copy and paste from the right click menu.

b. Enter the input file name
c. Result type is set to BMP.
d. Set Blob Threshold Minimum. I use 50 on imagery which has a resolution of 1.2m/px and 100 on 0.5m/px.
e. Set Blob Threshold Maximum. I usually set to 0.
f. Grab some coffee.
g. Your output file should now be in the same directory with
_br50_0.bmp at the end.

10. Import cleaned raster map and convert it to a vector shapefile.

a. Click on the GRASS shell icon, type in, browse to your image file & enter the raster map name.
b. In the Optional tab check
Override Projection and Force Lat/Lon Maps to fit into geographic coordinates. This will allow for import of an unrectified image.
c. Click Run.
d. Click on the GRASS shell icon, type in
r.region, select the recently imported image and in the Existing tab check Set from Current region and click Run.
e. Now drag and drop the image from the QGIS Browser to the QGIS Desktop to confirm that it has been loaded and rectified correctly.
f. Click on the GRASS shell icon, type in
g. Select your input map.
h. Give your output vector map a name, example “TBPB_reclass11_br_50_0_vector”
i. Set Output feature type to
Area and Click Run.
j. Click on the GRASS shell icon, type in
k. Select the vector to export, provide a directory and file name, example, “TBPB_reclass11_br_50_0_vector.shp” and select ESRI_Shapefile as the Data format to write and click Run.
l. Take a sip of that coffee.
m. Drag and drop the shapefile from the Browser to QGIS Desktop for viewing.
n. Click on the
Toggle Editing button and then the Select by Expression Button.

o. Enter the expression “value” = 0 or “color” = 0 to select all polygons which were black areas, then delete those polygons.

p. Go to Vector Menu > Geomtery Tools > Simplifiy Geometries…
The Default value of 0.0001 should work well. Save to a new file, example “TBPB_reclass11_br_50_0_vector_simp.shp”. Click OK.

11. Export Autogen using Scenproc.

It’s finally SCENPROC time!
Run similar code through ScenProc. FWIDTH and FLENGTH ensure that only the smaller buildings for this area are generated. You can choose to leave this out.

#Barbados 2017 autogen

ImportOGR|D:\GRASSDATA\TBPB_reclass11_br_50_0_vector_simp.shp |*|*|NOREPROJ

EXPORTAGN|P3D v2| C:\Program Files (x86)\Lockheed Martin\Prepar3D v2\Addon Scenery\RS Barbados 2017\Texture

12. Open the corresponding area in Annotator. Now would be a good time to clean up any poorly placed/sized buildings.

No more drawing thousands of rectangles for you!!!

13. Open your simulator and marvel at how far you could possibly take your autogen!!!

Throw in some “Fully-Automated Tree Extraction from Satellite Imagery for Autogen Creation in FSX/P3D using ScenProc” and you can hardly tell that you didn’t place those buildings manually!!!


Resource contributor
Correct. No geodata of the area is required. Only satellite imagery.

One step that I forgot to include is the removal of holes from polygons in the shapefile. That tends to improve results slightly.

Will include that in a revision.
I am impressed with the results. How long did it take QGIS to covert from bmp to SHP? The issue I see would be clean-up. That would take a long time. Maybe longer than the creation process?


Resource contributor
Indeed, the longest processes are by far the raster to vector conversion and the removal of holes from the resulting shapefile. However, with this working area I have never sat for hours waiting for QGIS processes to end.
The blob removal tool cleans up just as quickly; all these processes take several minutes on my machine if I remember correctly.
Overall, the benefits of time saved in automation compared to manually annotating tens of thousands of buildings far outweigh the slight inaccuracies in density and placement.

EDIT: Just ran a quick test of the conversion... took 6 minutes on the above 22000x21000px image; about 150 sq/km @ 0.5m/px
Last edited:
I installed QGIS but do not have the GRASS option under Plug-ins? Nor is it available to activate when you open Plug-ins? How did you get GRASS to show up? I just installed the latest version of QGIS v2.16.1.



Resource contributor
It's been a while since I used this process, but if you give me a few days I should be able to find time and revise it for GlobalMapper.

Many thanks... there are some Global Mapper users here at FSDeveloper who would welcome seeing that additional alternate work-flow. :)

Are we able to sticky this please? Might need to add the --UI argument is required of the GRASS commands in 2.18.9+ to get the UI elements to show as described in the process, else they give you the command line help dump.
Can you combine multiple tiles into one subgroup and process them all at once? I tried the obvious method of simply dumping them all into a subgroup, but the command:
i.cluster group=grp_TAS-SE@ms_TAS_SE subgroup=sgrp_TAS-SE signaturefile=sig_TAS-SE-15 classes=15 reportfile=D:\Data\GIS\Tas\TAS-SE\EXPORT\GEO\SIGS\sigf_TAS-SE-15
ERROR: Not enough non-zero sample data points. Check your current region (and mask).

I used a region that encompases all the tiles (3 bands/tile x 44 tiles @ 20000x20000px ea) with the command:

Doing each tile separately in it's own subgroup seems a little inefficient, but I don't know enough about QGIS or GRASS to really know what it is doing here in detail (I normally use Global Mapper 18 which has a completely different nomenclature and process flow).


Last edited:


Resource contributor
Hi Braedon (and @GaryGB), It's been a while since I've used this method, but I now realize 2 things:
1) This method utilizes the classification tools of GRASS, thus QGIS will HAVE to be used up until at least step 8. GlobalMapper could be used afterwards.
2) @Bungo I have never tried using more than 1 tiles, but it looks like the g.region raster command needs ALL of the 44 tiles that you would be using in the following format. [raster=name[,name,...]]

I am not sure if the g.region UI provides an option to select bands from multiple imagery tiles imported into GRASS but you could also explore that option.
I sort of worked around the issue a little. I used gdalwarp to mosaic a 2x6 grid, a 4x6 grid and a 3x4 grid of my 20000px x 20000px 'tiles' and will process the 3 areas separately. Gdalwarp grinds to a halt with too many tiles/too much data. if you increase the -wm value it gives integer overflow errors. Seems to be limited to 32-bit address space. I tried to split the image in such a way that the borders between the 3 columns of imagery do not occur in heavilly built up areas so that there isn't a line of partial buildings, and thus a line of vacant lots in the scenery. Paranoid?...maybe... :)

In a GRASS shell (or several if you parallelise the process), repeat these steps below to cover the area required in sections, in the case below, 3 columns. Limiting the display in this post to labelled "COL1", but it is repeated for all the sections (or my columns).

// Mosaic a section into one big TIFF with GDALWARP
gdalwarp -wm 2000 -multi -wo NUM_THREADS=ALL_CPUS --config 'GDAL_CACHEMAX=2000' TAS-SE-RA_A_01.TIF ...<etc. etc.>... TAS-SE-RA_F_02.TIF TAS-SE-RA-COL1.TIF
// The last image is the output file, all the previous ones are input files making up a complete rectangle, all file names separated by a space
// Load the raster into the GRASS database input=TAS-SE-RA-COL1.TIF output=TAS-SE-RA-COL1
// Assign the imagery bands to a group+subgroup group=grp_TAS-SE-COLS subgroup=sgrp_TAS-SE-RA-COL1,,
// set the region coordinates to be the bounds of the imagery
// Do this next step repeadedly changing the number 15 between 9->17 to obtain the signature files for classificaton steps (section 7.a->d)
i.cluster group=grp_TAS-SE-COLS subgroup=sgrp_TAS-SE-RA-COL1 signaturefile=sig_TAS-SE-RA-COL1-15 classes=15 reportfile=sigf_TAS-SE-RA-COL1-15.txt
// clasify the imagery based on the classes from the previous step. Repeat for all the classes run in the previous step.
i.maxlik group=grp_TAS-SE-COLS subgroup=sgrp_TAS-SE-RA-COL1 signaturefile=sig_TAS-SE-RA-COL1-15 output=class_TAS-SE-RA-COL1-15

I added a couple of black 'tiles' to make the grids complete so no corners or holes exist with null values in the region, which was probably unneccessary as the mosaic would have filled the gaps in with black.

Seeing how that goes, still processing.

However, if BREC requires .BMP format, I'll have to work out how to take the data that is larger than a BMP (as a 32-bit format) can cope with.
Last edited:


Resource contributor
Any luck @Bungo ?

P.S: If the BMP format would limit the size of tiles you could work with, you could possibly try using another format in Photoshop and skip the use of BREC. Photoshop's "Magic Wand" selection tool and "Contract Selection" command could be used to remove small specs. However that will require you to manually edit all 44 tiles unless you can script this process.