Download FlightGear 2019.1.1 for Windows (versions 7, 8, 10) Download FlightGear 2019.1.1 for MacOSX. Continuous oblate ellipsoid world available for you to explore. Our terrain is based on 90m SRTM data and is very detailed. There are over 20,000 airports you can visit world wide. In the event that the primary FlightGear.
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Landsat DataNow we take care of the Landsat data, which is a different kind than SRTM. The best freely avaialble data source for Landsat7 data is the EarthSat version available through GLCF. It is already ortho-rectified, but still provided in bands so that there is no true-color version available out-of-the-box.True-color versions exist but the data providers make a fortune out of selling those although they are just a processed version of otherwise freely available data that has been remotely sensed by the NASA with US government funding. So it does not appear sound to pay for something that has been funded by the public. Access to the public Landsat data should be completely free to the public!This is why we are going to prepare and publish a free true-color version of the original data.So first we are going to download all necessary bands of the EarthSat Landsat 7 data from the GLCF server. Then we are going to process the data with libGrid to yield naturally looking images with world-wide coverage at 15m resolution. This processing step is known as.
More about those processing techniques in the tutorial. Acquiring the Landsat DataThe Landsat data cannot be downloaded manually, since it is more than 1.5 terabyte of compressed data.
Even through a good internet connection with a consistent bandwidth of say 1MB/s the download takes more than one month. In practice, I get a download speed of about 400kb/s from the.Therefore, we clearly need to automate the Landsat data download. We are am using a shell script for that purpose. It loops over all bands of the Landsat images that are available on the GLCF server and downloads them one by one with the GNU wget tool.The following does the job.
Compressing the Landsat DataThe Landsat data is available in a lossless compressed format. One patch with compressed bands 1,2,3,4 and 8 is 234MB on the average. There are roughly 8000 patches covering the entire world.
This amounts to about 1.5 terabyte in total.While there is the general rule to store original data with lossless compression, the data is so huge that considering the option of a slightly lossy compression makes sense.One patch of band 8 is about 230MB of uncompressed and about 80MB compressed data. When considering lossy compression, we want to gain just a slight additional compression ratio of about 1:2, so that the impact on image quality is minimal. For those low compression levels, the JPEG format is a viable option. The GEOTIFF format supports the so called JPEG-in-TIF option where GEOTIFF is used as a container for embedding a JPEG image while keeping the geo-referencing information, which is otherwise lost in pure JPEG.The following table shows image quality vs.
Compression ratio with the JPEG-in-TIF option of the GeoTiff library of GDAL for path 63 row 46 band 8 showing Big Island of Hawai’i (crop area 1000×1000+8500+8500). CompressionSizeCompression ratioRelative ratioImage qualitynone233.0MBoriginalLZW83.0MB1:2.77originalJPEG 9527.4MB1:8.501:3.07almost as crisp as originalJPEG 9016.0MB1:14.61:5.25fine details are blurred, less visible contrastJPEG 808.8MB1:26.51:9.55too much detail lossIt appears that JPEG compression level 95 is the best compromise to gain a compression ratio of 1:3 while loosing just a bit of image crispness compared to the original data. The JPEG compression even smoothes out some graininess of the original data.Supposed we want to save a single geotiff image file in the JPEG-in-TIF format, we compress it on the Unix command line by running the gridcopy tool of the library. Saving Compression TimeThe compression of a single patch with bands 1,2,3,4 and 8 to the JPEG-in-TIF format takes about 7 minutes.There are roughly 8000 Landsat patches covering the entire world. Extrapolating the time to compress that amount of data yields 40 days of computing time. On a dual-core processor this goes down to three weeks.
Ok, that will not finish over night but it seems reasonable for the huge amount of world-wide data.To parallelize the conversion we use the -j option of GNUMake, which is able to run multiple make jobs concurrently.The following Makefile converts all images with the ending.tif.gz in the SRCDIR directory to JPG compressed images with the ending.jpgintif in the DSTDIR directory. It assumes that the libGrid gridcopy tool is available and located in the TOOLDIR directory. SRCDIR= EarthSatDSTDIR= compressedTOOLDIR= /Projects/libgrid/grid/toolsTOOL= gridcopyTOPTS= 95TIFS= $(wildcard $(SRCDIR)/.tif.gz)JPGS= $(patsubst $(SRCDIR)/%.tif.gz,$(DSTDIR)/%.jpgintif,$(TIFS))all: output $(JPGS)%.jpgintif:@echo processing $.jpgintif@-gunzip -c $(patsubst $(DSTDIR)/%,$(SRCDIR)/%,$.).tif.gz $.tif@-$(TOOLDIR)/$(TOOL) $.tif $.jpgintif $(TOOLOPT)@-rm -f $.tifoutput:@-if ! -e $(DSTDIR) ; then mkdir -p $(DSTDIR); fiRunning that Makefile with 4 parallel jobs on a quad core processor withmake -j 4is estimated to bring down computing time to less than 2 minutes per patch and less than 2 weeks in total. Color-Mapping the Landsat DataNow we take care of the conversion of the separate satellite bands to true-color rgb imagery.As outlined in the previous sections, we use the pan-sharpening tool of libgrid to produce the true-color imagery. #!/bin/tcsh -fset src = true-colorset dst = thumbs# World coverageset l7path1 = 1set l7path2 = 233set l7row1 = 10set l7row2 = 100# which images to thumbnailset ext = 'tif'set sfx = 'echo 'creating thumbnails for Landsat 7 ETM+ imagery:'if (! -e $dst) thenmkdir $dstendifset r=$l7row1while ($r = $l7path1)set l7path=p$pset l7row=r$rif ($p.
#!/bin/tcsh -fset src = thumbs# World coverageset l7path1 = 1set l7path2 = 233set l7row1 = 10set l7row2 = 100# which images to indexset ext = 'jpg'set sfx = 'echo 'echo 'echo 'Index of Landsat ETM+ imagery:'echo 'echo 'echo 'set r=$l7row1while ($r 'while ($p = $l7path1)set l7path=p$pset l7row=r$rif ($p 'if (-e $l7thumb1 && -e $l7thumb2) thenecho -n 'endifecho -n '@ p-endecho '@ rendecho 'echo 'echo 'Here is how the resulting HTML thumbnail matrix of entire Europe looks like. Fixing the Landsat DataAfter processing the Europe subset, it appeared that some patches were darker than others.An example for that flaw are the two patches p193r030nn10 and p193r031nn10 (covering part of Korsika). One is bright, the other one is much darker.The.met files, which accompany each set of Landsat bands, state different sun elevation angles for the two Korsika patches with 57 and 63 degrees ( and ).
But that alone cannot account for the large variation in brightness.When looking closer at the.met files, it came out that the patches were normalized from their original radiance values to so called calibrated digital number images (QCAL DN in the notation of the USGS). Since different patches have different radiance range, the calibrated range will stretch differently after normalization looking darker or brighter.As a consequence, we need to reverse the normalization. Fortunately, the original radiance values are given in the.met files, so we just need to apply those values as a scaling factor to the calibrated original data.I added support for the Landsat band radiance correction to the libgrid library so that it tries to read the appropriate scaling factors from the.met files in order to re-adjust the intensities. Those adjusted intensities are called at-sensor reflectances.The scale and bias factors applied are defined as follows (for normalized reflectances in the range 0.1). Color-Mapping the Landsat Data (Continued)Here it goes again.
We recompute the entire subset of Europe with the corrected at-sensor reflectances, to see if that finally works out well.Started recomputation on 20.2.2014 3pm Finished recomputation on 24.2.2014 11pm. Significantly faster than the first run, probably because I forgot to compile in release mode the first time.And was it worth all the tweaking? Yes, the brightness of all Landsat true-color composites is now perfectly balanced! The same holds for the topographic Landsat composites. Configuring the NAS (30.7.2014)To configure the Synology NAS, we go to the web interface at and log in as “Admin” with an empty password. Then we follow these steps:. Enter the EZ Internet Assistant.
Enter and confirm the network settings. In the firewall settings allow NFS (Mac/Linux file server). Enter the system settings. In the “Services” section, enable the NFS file service. In the “Shared Folders” section, create a new shared folder, e.g.
“nas”. Edit the folder settings.
Edit the NFS priviledges of the folder. Enable NFS priviledges for the local sub net, e.g. “141.75.33.”. Enable read/write priviledges. The mount point of the shared folder is “/volume1/nas”.
If everything worked well, we override the default admin password. Producing World-Wide Data (Finally)The production of true-color imagery from world-wide Landsat data is now running on a 4 core Xeon.Summing up: Each Landsat patch has been downloaded by a to my GLCF mirror server at (7866 patches with 1.5TB). For each Landsat path the required bands are transferred via ssh from my GLCF mirror server to a 4 core Xeon Linux box. Then the bands are merged with the libgrid merger tool. Merging is scheduled in parallel using a parallelized.
And finally the merged true-color imagery is uploaded to the NAS, which is mounted per NFS.Estimated run time for all 7866 Landsat patches is 5 weeks starting on 8/11/2014 for a total a 5TB of true-color imagery.
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