Michael produced these great plots below showing the fan and blotches identified in each subject image showing 6 overlaping subjects. We have overlap to ensure that we don’t miss marking features at the edges of subject images.  We had to cut up the HiRISE images into smaller chunks in order to get the resolution needed  and make the Planet Four website as easy to use as possible.

Each color in the plot below represents a Planet Four subject image. The dashed blue lines are to show the overlap region boundaries and the solid blue lines are the boundaries between the subject images.

These are all from Ithaca where the fans tend to be very wide. It’s a very flat region on the Martian South Polar region, which might have something to do with it. So it’s one of our best cases to look at what we should do about combining the sources from different subjects in the overlap region.

Looking at these plots, we see fan directions aren’t impacted. That although in some cases we have to fans or two blotches on top of each other with different widths and extents, the direction of the source is well represented. By using shapely we’ll be able to deal with this. For the project’s first paper, we’re focused on wind directions so we’re calling the catalog done for now and will do the Shapely stage next for fan and blotch areas and counts.

We can now confidently turn your clicks into wind directions. This is a big milestone for the project. It means we get on to writing the second half of the Planet Four paper, talking about the catalog and what we see for wind directions.  Onwards and upwards!

I thought I’d share a figure from last week’s science team call that the science team discussed.  Michael was looking at combining clustered features with Shapely, a Python package for manipulation and analysis of planar geometric objects. Partly this is to investigate whether this could be used to deal with differing clusters in the overlap regions between neighboring subject images and also test out if we can use the software package to easily calculate the total area covered by the seasonal fans and blotches.  Shapely does a good job of merging the blotches together as you can see from the figure below. This definitely looks like a way forward for calculating the total surface area per time of year covered in dark fan material.

Image credit: K-M aye

We’re now working on dealing with the last major component of the Planet Four data processing pipeline, the overlap regions of neighboring subject images. We divide each HiRISE images into many smaller 840×648 pixel subimages or subjects that we show on Planet Four. To make sure we capture fans and blotches that are the edges of our subject images, we have a 100 pixel overlap between the neighboring left and bottom subject images. This means that we have duplicate markings that cover the same source which we need to identify as being the same source to allow for counting the number of seasonal sources and to also accurately measure the shape of very large fans or blotches.

We spent part of the last science call looking at some examples of overlap regions and the outputting fan and blotch shapes after clustering to decide what to do.

Image credit: K-M. Aye

If you focus on the center sources in the two plots above, you see there are lots of markings identifying the same shape from the different subject images that contained varying parts of the central  blotch or fan. Based on what we see, we think we if in the overlap region we only keep the largest source and anything that extends beyond that we will accurate identify the fan or blotch being marked. We’re going to test that this week and review the output from the catalog for a small portion of the overlap regions to confirm.

Once we sort what to do in the overlap regions, the focus should be writing all of the steps in the processing pipeline into the paper draft.