I was helping to prepare a presentation for a local high school, and I thought it would be cool to show them a picture of their school as HiRISE would see it. My first thought was the satellite layer in Google Maps. So I zoomed way in and took a screenshot. I wasn’t able to find a reference for the pixel scale of the satellite imagery (if anyone knows of one, please leave it in a comment!), so finally I just figured it out myself by using the Distance Measurement Tool. Turns out, if you zoom in as far as possible, the satellite images have almost exactly the same resolution as HiRISE! (This is true in Tucson, anyway; the coverage varies over different locations.) I thought this was a great way to visualize just how awesome HiRISE images are – just imagine looking at Mars like you can look at your home town on Google maps! …I guess that makes the rovers like Mars StreetView.

This is my neighborhood as HiRISE would see it: (Look at all those pools! Tucson is not nearly as dry as Mars )

While spending a little time working on various scalebar utilities, I thought I’d take a look at the distribution of image sizes. We’re at around one thousand images (TRA + PSP) of Mars. This is over a half million megapixels in total, not counting calibration images.

The first plot is a histogram of total pixels. Most frequently, images are under a quarter gigapixel, but there are quite a few between 750 and 1000 megapixels. And it has been uncommon to take images larger than a gigapixel.

The next plot is a histogram with 2-D bins, showing the frequency of width and height (in pixels).
The more popular imaging modes are colored brighter. So the most common dimensions are around 20,000 by 40,000 (the yellow box). Most images are the full width (20,048 pixels), followed by half width, etc (the columns). This is simply a function of the binning (subsampling) modes, but can change due to missing or unusable channels. However, there is a good deal of variability in the heights, which corresponds to the exposure duration (and also the binning). At least one image had over 120,000 lines!

These plots were generated with ploticus, a free-software plotting utility.

Over at NASA Ames, the HiRISE Clickworkers program is in beta-testing. Anyone (this means you!) with a browser and a net connection can participate in the cataloging, or more precisely, keywording of HiRISE images.

This is an ambitious effort. Originally (years before HiRISE), Clickworkers was used to tag craters on Mars, helping pin down the relative ages of various regions. This time around, you identify a dozen or so possible feature types, then move on to the next image. So you have to be a little more discerning, though examples are provided.

I was just looking at the sizes of our images to date. We’re coming up on one thousand images that have been map projected. And it looks like we just recently passed the one million megapixel mark (one thousand gigapixels, or one terapixel!) in the geometrically projected ones (when rotated so that North is up, there tends to be a lot of empty pixels framing the images).

Assuming a standard screen size of 1.25 megapixels (1280×1024), that is 800,000 screenfuls. If you looked at one per second, it would take you almost ten days to view it all! But one thousand volunteers could get through it in a day, and spend 100 seconds per image, which seems reasonable. [Though of course they'll need time for sleep, etc!]

The idea of using human brain power as a sort of massively distributed computation engine (shades of The Matrix) has come a long way. Amazon’s Mechanical Turk pays volunteers for tasks such as identifying features, translating documents or answering questions. It was recently used in the search for a person (computer scientist Jim Gray) missing at sea. Volunteers viewed over a half million images, covering 3,500 square miles of ocean, though unfortunately his sailboat did not turn up.

Still, ‘crowdsourcing‘ (as Wired called it) seems like it will continue to be an efficient way to perform tasks that computers are currently very poor at. Here at the Lunar and Planetary Lab, it has also been used by Spacewatch to find Earth-approaching asteroids. So, essentially, you could help save the planet in a real-life version of the classic game Asteroids! Clickworkers also has a program where you can tag Mars Global Surveyor images, scouting interesting locations for HiRISE to target.

We can’t let the machines have all the fun!

Tuvas posted this great top-ten list over on Unmanned Spaceflight.

I changed it around a bit, hope you don’t mind, T!

1. You consider any image with less than a billion pixels a mere pittance… a negligible amount of data.
2. You realize that any part of Mars can be interesting, if viewed at sufficiently high resolution.
3. You start to see in black and white away from the “Center strip” of your eyes.
4. You have decided to buy a 500 Gigabyte drive just to store a few dozen of your favorite HiRISE Images.
5. You’re considering getting a new 40″ LCD mainly to look at HiRISE Images.
6. You know what JPEG 2000 is.
7. You start making up new Hi Names (HiStuff, HiSpace, etc, etc).
8. You continually refresh the web page starting Wednesday morning, waiting for the next release.
9. When using Google Earth, you wish you could zoom in further, just like HiRISE can.
10. You’re reading this!

To conclude our exploration of the pipelines that take raw channel files and create a beautiful, unmapped mosaic, let me introduce the Stitch pipelines: HiStitch and HiccdStitch.

The HiStitch pipeline combines the matching HiCal products for the same CCD into one more-or-less lined up CCD cube file. HiccdStitch combines these HiStitch cubes into RED, IR, and BG mosaics.

Both pipelines take some time, as overlapping pixels are accounted for and brought together. After these mosaics are created, additional steps create smaller jpeg files for easier viewing, and full-sized jpeg2000 files. We use these jpeg2000 files for validating our images.

There are later pipelines, but we first validate the HiccdStitch products: Did the previous pipelines work correctly? Did the uplink team command the camera correctly? Is there haze or clouds obscuring our view of the surface?

If everything looks good, and we have received the correct reconstructed SPICE ephemeris data, then the geometry pipelines are invoked. These pipelines project the images mathematically to a model of Mars and add geometry data to the images so that each pixel becomes a point on Mars with latitude and longitude coordinates.