My top pick is NASA’s new integration of the Apollo Zone Digital data. It was done at Ames Research Centre thanks to a newly developed software system for orbital imagery. The software allows fully automated image mosaicking and terrain modeling of data taken from different positions, with different exposure and resolution, and even selects best image when multiple coverage exists. You can read about this exiting new development in the article Powerful Pixels: Mapping the “Apollo Zone” which has links to the open source software libraries Ames Stereo Pipeline, Neo-Geography Toolkit and NASA Vision Workbench. You can click here to download a kml file for viewing the image mosaic and digital elevation model in Google Earth. I tried it out and it looks great. Check these screen captures below:

You can switch to polar view with a single click. Below here I am showing the average temperature.  It is great to have the option to use opacity (in this example I applied an opacity of 0.4) but on the down side I could not find a colorbar to turn on.

A great feature is the crater search:

There’s a video demo available: Lunar Mapper LMMP Demo final.mov

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My second pick is  The Essential Geography of the United States of America from David Imus. Imus put together an innovative new map of the United States, which is really good news. Why is this a big deal?  There’s an excellent article about it on Slate magazine so I point you to that   for a full review .

What I think is worth mentioning – and I like a lot in this map is that Imus used (simplified) Relief Shading to render the terrain, without sacrificing any of the requirements of a commercial cartographical product (labels, boundaries, etcetera). Here’s a quick comparison I did of a Bathymetry Map of the Hawaiian archipelago from USGS (top, converted to grayscale) and the provided for Hawaii on Imus Geographics website (bottom, also converted to grayscale).

I would encourage you to take a look at  Imus’ very instructive Booklet on advancing geographic literacy in classrooms, and to watch his YouTube interview.

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Bronze medal goes to the paper Adaptive smoothing for noisy DEMs which was posted on GIS and Science blog in December. I think it would be very interesting to try the method on geophysical data (for example structure maps from reflection seismic), where often the noise level is spatially variable.