Greeting from Tucson Arizona. I’m here with Planet Four PI Candy Hansen at the Building the NASA Citizen Science Community Meeting. The aim of the workshop is to bring together researchers engaged in successful citizen science projects, citizen science experts and platforms supporting citizen science projects (including representatives from Zooniverse), the NASA Science Mission Directorate, and researchers interested in applying citizen science to their research problems.
I gave an invited talk (my slides are included below) highlighting science results and the success of Planet Four and advertising Planet Four: Terrains and Planet Four: Ridges. It’s exciting that people in the planetary and astronomical community see Planet Four as a successful project. That is in large part due to the contributions of the Planet Four volunteer community. It was great to talk about Planet Four’s first paper and also mention the science team is working on three other publications right now based on the first fan and blotch catalog.
Today we have a guest post from Dr Eriita Jones and Professor Mark McDonnell. Eriita is a Planetary and Space Scientist, Research Fellow at the School of IT and Mathematical Sciences, University of South Australia, and an ECR member of the National Committee for Space and Radio Science. Her primary research areas are (i) the remote detection and characterisation of subsurface water environments on Mars and Earth, and (ii) quantifying the habitability of other planetary bodies. She is particularly interested in new computational data analysis techniques and in assessing the benefits of machine learning for space science. Mark McDonnell leads the Computational Learning Systems Laboratory at University of South Australia. He has published over 100 research articles in the fields of machine learning, computational neuroscience, and statistical physics. Mark has worked extensively with industry partners to deliver applied machine learning solutions in areas such as precision agriculture, recycling, and sports analytics. His research interests lie at the intersection of machine learning and neurobiological learning.
Artificial intelligence may get some bad press, but there are of course many tasks with which AI can provide tremendous benefit to human beings. One of the tasks that AI can be utilised for is called ‘image segmentation’, which is the process of automatically dividing an image into objects or categories so that every pixel in the image receives an associated label (e.g. car, dog, tree). This is essentially what the Planet Four citizen scientists are doing when they manually outline the boundaries to fans and blotches in polar springtime imagery from Mars. Just like a human being, in order to learn a new skill a machine needs to be taught (or ‘trained’) in the task it is being asked to perform. For state-of-the-art automated image segmentation, this training requires large amounts of data in the form of images with the categories of interest clearly labelled. In 2018, researchers at the Computational Learning Systems Laboratory at the University of South Australia in Adelaide, Australia, realised that large amounts of labelled imagery was exactly what the citizen scientists on the Planet Four project were generating. That was the start of a collaboration with the Planet Four Science Team. We wondered – could we teach an algorithm to automatically detect fans and blotches in Martian imagery? How well could a machine learn these complex features? And could the algorithm provide information which would assist the scientists in their study of these Martian phenomena?
The machine learning algorithms used here are examples of deep Convolutional Neural Networks (CNN’s) which generally perform very strongly on image segmentation problems. The algorithms are fed thousands of labelled fan and blotch images produced by the Planet 4 citizen scientists. After lots of exposure to what fans and blotches look like at different locations, years, solar longitudes, and resolutions, the algorithms become able to generalize from their experiences and apply their learning to new situations – in this case, unlabelled images that they have never seen before. In order to assess how well the machine learning techniques are performing, the algorithms are given a test. They are asked to predict where the boundaries of the fans and blotches are in some labelled images – but the algorithms are not shown the labels and have never seen those images before. We can then compare the machine’s predictions with the ‘correct answers’ – the manual labels drawn by citizen scientists. We compare with another method as well– a more traditional and less complex image classifier that does not employ machine learning. The figures below shows the output on a subset of one HiRISE image.
We are busily working on validating the output of the machine learning algorithms on a large number of images, but we can already see ways in which they can be very useful. Although the algorithms might not always find every fan or blotch in an image, they are very good at deciding whether there is at least one feature present. In other words, they do a good job at sorting out the images which have a fan or blotch, from those that have no fans or blotches at all. This is a very useful way of streamlining the presentation of images to the Planet Four Zooniverse platform – for example, instead of having to click through ‘featureless’ images the Planet FourTeam in future may wish to make sure that every image that appears will have a fan or blotch in it for labelling. Additionally, by automatically predicting the presence of fans and blotches in new images the algorithms provide early information on feature number and density that can allow the Planet Four team to be more selective in which images have the highest priority for manual labelling.
Could machine learning one day put citizen scientists out of a job? We don’t think this is very likely. The algorithms may eventually learn to perform very well on new images if those images are similar enough to ones they have seen before. But if they are shown an image that is very different (e.g with unusual lighting conditions, strange background terrain, or uncharacteristic fans and blotches), it is likely that the machine won’t be quite as good at segmentation as a well-trained human eye. So don’t worry citizen scientists, AI is just here to lend a hand – thanks for all the fabulous data, and stay turned for an exciting update in a few months!
The science team is working on migrating Planet Four to the Zooniverse’s more modern project builder (or panoptes) platform. This is a slow process because things are different in how the newer Zooniverse platform displays images and also we want to take the lessons we’ve learned over the past 6 years and use it to make the web interface even better. We thought we’d share some screen shots from our work-in-progress prototype.
The next stage will be getting some images on the site and beta testing the changes we want to make and seeing how well these tweaks do compared to the current Planet Four website/classification interface. This might take a few months, but we’re working hard to have this ready before the end of the year.
In the meantime, we have new images on the original Planet Four website that we are hoping to get classified before the older Zooniverse platform that runs the current Planet Four site is officially retired. We’re trying to make the push in April to get these new images classified. If you can spare a few minutes to classify an image or two on the main Planet Four site, we’d appreciate it.
Today we have a post by Candy Hansen, principal investigator (PI) of Planet Four and Planet Four: Terrains. Candy also serves as the Deputy Principal Investigator for HiRISE (the camera providing the images of spiders, fans, and blotches seen on the original Planet Four project). Additionally she is a member of the science team for the Juno mission to Jupiter. She is responsible for the development and operation of JunoCam, an outreach camera that involves the public in planning images of Jupiter.
We have discovered something very interesting in the number and size of the fans that show up on the south polar seasonal cap every spring, that you are measuring. It turns out that in springs following both global and regional type A dust storms we see a lot more fans than normal for that time of year. This picture compares sub-images from 7 Martian years taken in “Manhattan” at solar longitude 195-197. The position of Mars in its orbit is the solar longitude (“Ls”), and southern spring begins at Ls 180 when the sun crosses the equator and heads south. Mars years 29, 30 and 33 have visibly more fans. There was a global dust storm in Mars Year (MY) 28 that started in early summer. Intense Type A storms, which are regional and centered at high southern latitudes, took place in MY29 and MY32. It looks like the spring after these storms have large numbers of seasonal fans.
Although the visual impression is powerful when these images are compared we can go beyond that now, thanks to the Planet Four fan catalog that your work has populated. We can quantify the differences. We used the MY29 an MY 30 catalog that we’ve published this year in our first paper, and also newly generated catalogs for Manhattan for MY 28, MY31, MY 32. Instead of just saying “there are a lot more fans” we can say “there are over twice as many fans” in MY29 and MY30 compared to MY28, 31 and 32. We do that by querying the catalog – an example is shown below. The plot below shows numbers of fans as a function of time in the spring and we can compare 5 years at Ls 195. I had the pleasure of presenting this (your!) work at the 2019 Lunar and Planetary Science conference last week in Houston, Texas.
To confirm that Type A storms are playing a significant role in the composition of the seasonal ice sheet that produces the carbon dioxide jets that bring up the dust and dirt that create the seasonal fans and blotches, we need to look at the number of seasonal fans and the area covered in MY33. We only have classifications for Seasons 1-5 of the HiRISE seasonal monitoring campaign (MY28-32). This brings me to my request: We would really like to have Planet Four measurements for MY33. We have uploaded the images, so it is ready for you to process. We would like to thank you in advance for your generosity with your time. Once those measurements are in we will be ready to write our next paper documenting these findings in a peer-reviewed scientific journal. As you know we have published one paper already and two more are in progress. This is a significant result, and we could not have done this without all of you.
Help classify the new images of Manhattan today at http://www.planetfour.org.
Happy Birthday Planet Four. This month marks 6 years of Planet Four. We couldn’t do any of this without the Planet Four volunteer community. Thank you for all of your help and contributions. We hope you’re celebrating with a slice of cake or a serving of Mars pie. The team is really excited for what’s to come next. We’re working hard on follow-up papers to the first fan and blotch catalog release. We’re also starting preparations to move the project to the Zooniverse’s newer Project Builder Platform. We’ll keep you posted on all of these efforts right here on the blog. Lots more to come in 2019!
Greetings from Knoxville, Tennessee. Earlier this morning, I presented our first catalog and early results from comparing the fan directions over two Mars years at the American Astronomical Society’s Division for Planetary Science meeting. Here’s my slides.
Dear fellow Planet-Four-ians,
It is my great pleasure to announce that the Icarus journal has accepted our paper “Planet Four: Probing Springtime Winds on Mars by Mapping the Southern Polar CO2 Jet Deposits” for publication!
The edits requested by the reviewers were minor, we addressed what we thought was appropriate for the already huge scope this paper tries to encompass and the editors agreed to our submitted revision. I have also updated the arXiv preprint version with that submitted revision and it is now available in its final “content” form here: https://arxiv.org/abs/1803.10341. We publicly acknowledge everyone who contributed to the classifications that went into this paper and gave us their permission to use their name on the page https://www.planetfour.org/authors.
We now have entered the phase of typesetting the article where the formatting towards the style of the journal is happening and things like placement of figures is being decided on.
Next in line of activities for Planet Four is waiting for the selection of NASA’s Solar System Workings proposals, where we submitted in spring to receive funding for a deeper exploitation of the results of Planet Four and to use it to guide the creation of a geophysical model of CO2 jets. We expect that the selections are made in the first half of September, according to recent information we have received.
Fingers crossed that we can continue further together on this exciting venture!
Tag an image or two at https://planetfour.org !
Southern Spring is coming to Mars very soon. May 22nd marks the official start of Spring at the Martian South Pole. We’ve been busy reducing the most recent sets of classifications from Planet Four: Terrains looking for new spider locales to target when the HiRISE and CaSSIS seasonal campaign starts. The CaSSIS camera is a recent addition to Mars, aboard the European ExoMars Trace Gas Orbiter (TGO). It takes slightly higher resolution images than the CTX, whose images we show on the Planet Four: Terrains website. CaSSIS is designed for stereo imaging which is key for measuring depths and heights of features. Also unlike CTX, CaSSIS is equipped with several filters so color images can be made. Even with the addition of CaSSIS, the decade old HiRISE remains the highest resolution imager (~30 cm/pixel) in action around the Red Planet.
The PI of Planet Four: Terrains, Candy Hansen is a member of the HiRISE and CaSSIS science teams , and can ask for images to be potentially taken of the Solar Polar region if we find something interesting worthy of followup observations. We’ve asked for a few additional candidate spider locations (plotted below between -70 and -75 degrees latitude) outside of the South Polar Layered Deposits to be imaged if the observations can be squeezed into these cameras’ packed schedules. If confirmed in the higher resolution images, these will be the furthest spider identifications from the South Pole. Fingers crossed we’ll get some more detailed images of these places over the coming months.
Thanks for all your help. We plan to have new images on the Planet Four: Terrains site by the start of Southern Spring, so stay tuned!
We have finally submitted our first paper for the original Planet Four project to the Icarus journal where it is now officially “Under Review”!
(Above figure is one of the paper where we demonstrate one of the reduction steps to identify noise and create averaged clustered markings. I think it demonstrates well the power of our chosen methodology.)
Thank you to everyone to stay with us for so long without seeing any published results, but I think when you will see the work and care that we put into it, you will understand why it took us so long. One of the reasons was, as we possible mentioned before on this blog, that our Zooniverse project is actually one of the most difficult ones, where we ask all of you to precisely mark objects in the data presented to you. This required a spatial clustering pipeline with a long evaluation and fine-tuning phase.
Which brings me to the point of “see[ing] the work”: we have now managed to have the submitted preprint published on the well known arxiv.org preprint server and you can get your hands on a copy right now! Just click on this link and you will be sent to the arXiv page for our preprint:
Enjoy the (long!) read and don’t shy away to put any questions you have in the comments section below!
The science team is working on ticking off the last things on the todo list before we can submit the first Planet Four paper. Michael is in the last stages of making edits and changes to the paper draft. We’re nearly over the finish line. While Michael has been working hard on the manuscript text and catalog files, we’ve also been iterating on some changes to the figure Anya made that shows all the locations making up the Seasons 2 and 3 monitoring campaign that are part of our fan and blotch catalogs based on your classifications. I thought I would share some of the versions Anya made:
It’s really exciting to think back to when this project started in 2013 and now see this plot, where I can say we have fan and blotch identifications for HiRISE images taken in Season 2 and 3 Southern Spring/Summer for all of these plotted points.