Tiling the Blue Marble

Following on from my last post about level of detail and Earth spheroids, here is the NASA Blue Marble texture applied to the Earth:

The top level texture is the older composite Blue Marble which shows blue oceans and a very well-defined North Pole ice sheet. Once the view zooms in, all subsequent levels of detail show the next generation Blue Marble from January 2004 with topography [link]. Incidentally, the numbers on the texture are the tile numbers in the format: Z_X_Y, where Z is the zoom level, so the higher the number, the more detailed the texture map. The green lines show the individual segments and textures used to make up the spheroid.

In order to create this, I’ve used the eight Blue Marble tiles which are each 21,600 pixels square, resulting in a full resolution texture which is 86,400 x 43,200 pixels. Rather than try and handle this all in one go, I’ve added the concept of “super-tiles” to my Java tiling program. The eight 21,600 pixel Blue Marble squares are the “super-tiles”, which themselves get tiled into a larger number of 1024 pixel quad tree squares which are used for the Earth textures. The Java class that I wrote to do this can be viewed here: ImageTiler.java. As you can probably see from the GitHub link, this is part of a bigger project which I was originally using to condition 3D building geometry for loading into the globe system. You can probably guess from this what the chunked LOD algorithms are going to be used for next?

Finally, one thing that has occurred to me is that tiling is a fundamental algorithm. Whether it’s cutting a huge texture into bits and wrapping it around a spheroid, or projecting 2D maps onto flat planes to make zoomable maps, the necessity to reduce detail to a manageable level is essential. Even the 3D content isn’t immune from tiling as we end up cutting geometry into chunks and using quad tree or oct tree algorithms. Part of the reason for this rests with the new graphics cards, which mean that progressive mesh algorithms like ROAM (Duchaineau et al) are no longer effective. Old progressive mesh algorithms would use CPU cycles to optimise a mesh before passing it on to the graphics card. The situation now with modern GPUs is that using a lot of CPU cycles to make a small improvement to a mesh before sending it to a powerful graphics card doesn’t result in a significant speed up. Chunked LOD works better, with blocks of geometry being loaded in and out of GPU memory as required. Add to this the fact that we’re working with geographic data and spatial indexing systems all the time and solutions to the level of detail problem start to present themselves.



NASA Blue Marble: http://visibleearth.nasa.gov/view_cat.php?categoryID=1484

Image Tiler: https://github.com/maptube/FortyTwoGeometry/blob/master/src/fortytwogeometry/ImageTiler.java

ROAM paper: http://dl.acm.org/citation.cfm?id=267028 (and: http://www.cognigraph.com/ROAM_homepage/ )

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