11 - Danielle DeLatte: What can computer vision tell us about craters on other planets?

Imagine you're a rover on Mars, trying to find your way around. There's no towering skyscrapers or bodies of water to tell you where you are. What do you look for? The original natural landmarks: changes in land elevation that denote mountains and craters. On this week’s episode, Dr. Danielle DeLatte, a Space Systems Engineer at Draper, came in to talk about her PhD work using machine learning and computer vision to calculate the ages of craters and their possible role for rover navigation in the future.





Read on for the transcript!


Monte 0:07


Hi, I’m Monte Zweben, CEO of Splice Machine. You’re listening to ML Minutes, where cutting-edge thought leaders discuss Machine Learning in one minute or less.


This episode, our guest is Dr. Danielle DeLatte, a Space Systems Engineer at Draper. Danielle earned her PhD in aerospace engineering from the University of Tokyo, where she researched space applications of machine learning. Before earning her PhD, Danielle worked as an aerospace engineer at NASA Goddard. Welcome, Danielle.


Danielle 0:40


Thank you so much, Monte, I'm so happy to be here.


Monte 0:43


Well, this is great. I love speaking to fellow space people, former NASA people. I'd love to hear your journey: tell me about how you got to where you are now.


Danielle 0:53


Sure, I was going for the most degrees on most continents that I could possibly do. So I was extremely fortunate to study aerospace engineering at MIT, they have an absolutely phenomenal program. And when I graduated, I started looking at what what else was out there. And I found this really interesting program in in France called the International Space University, where we got a really interdisciplinary education. And that ended up being a theme for my career: interdisciplinary research, and projects. From there, I went to Goddard, as as you mentioned, then out to the University of Tokyo.


Monte 1:31


Excellent. Excellent. So tell us a little bit more about your PhD work: What are some of the applications of machine learning for space?


Danielle 1:38


One problem that I was really interested in was a planetary geology problem. Planetary geologists are really interested in how old a region is. So for example, on Mars, how old is a crater? Or how old is a region? So maybe there's a dry riverbed that's of interest. And so one of the really interesting parts of that was the interdisciplinary nature of the research, I got to work with Jack's Professor Elizabeth Tasker, planetary geologist, Dr. Sarah Crites, and machine learning researcher, Dr. Nicholas Guttenberg, to explore this problem and see how could machine learning help?


Monte 2:14


Excellent. And from a planetary geology perspective? How did you use machine learning to assess how old something is? On a planet like Mars or some other planetary object?


Danielle 2:27


That's a great question. So the way planetary geologists can tell how old a region is, is they they look at the number and the size of different craters. So what we were able to do with machine learning is look at Mars imagery and first of all, detect the craters and then count the ones in different sizes. And then from there, planetary geologists have come up with algorithms to determine at least the relative age for something like Mars. If you were looking at lunar images, though, you can actually use the samples that were brought back from the Apollo missions to do real age dating. But on Mars, we at least can do the relative ages.


Monte 3:03


Excellent. And that imagery, what frequencies or wavelengths [of light] were you using for that imagery; was that in the visible wavelength, or were there other other kinds of imagery being used?


Danielle 3:17


So we actually use infrared imagery. And there are a lot of different types of data sets that you could use, you could use digital elevation models, for example. So looking at how high each each pixel is, or, or you can, of course, use visible imagery. But we what we liked about the infrared is that there are less shadows and shadows turned out to be a really big challenge when we're looking at the craters. Because as you go toward the poles, the the craters look more and more extreme in visible imagery, but infrared had less of that issue.


Monte 3:50


Excellent. How did you formulate the machine learning problem?


Danielle 3:55


Well, we started with trying to detect where the craters were. So we were trying to detect the rims and basically find find a circular or elliptical outline of each crater. From there, we used computer vision methods to look at what the actual size was for each crater, and then use that to bin them and then determine the age.


Monte 4:17


Excellent. Did you use supervised learning to label edges that you knew were edges, and use that to train models?


Danielle 4:27


Yes, we fortunately, planetary geologists have been studying this problem for a long time. And so there are some fantastic labeled datasets. And so we we started with a really incredible data set that actually had 300,000 labeled craters on Mars. And that was, that was a fantastic start for for our machine learning algorithm.


Monte 4:47


That is fantastic. 300,000 labeled images that were probably labeled manually,


Danielle 4:54


Yes, labeled manually. If you can imagine for each of those craters in order to label manually, the researcher clicks on three points around the crater, or clicks in the center and then drags it out. So there there are some tools that help you do that. But it is a very manual process that we certainly benefited from in in the machine learning research.


Monte 5:16


That's very interesting. And now, with respect to the techniques that you used to model this supervised learning problem, did you use a deep learning neural network approach? Or did you use other algorithms? What was your basic approach to the classification problem?


Danielle 5:34


We used a segmentation algorithm called UNet. And what's unique about it among the compositional neural networks, is that from some of the training that happens in the early layers, comes back in the end layers. And so you there's an adding or merging process that happens. And so some of that earliest knowledge comes back at the end and helps helps bring the segmentation together.


Monte 5:59


Excellent. Thank you. With respect to the convolutional neural network, did you use tools to build that? Or did you build it from scratch?


Danielle 6:10


So we used Keras. And to be quite honest, I think if we were doing it again, today, we would probably use PyTorch. But at the time, Keras was the right tool. And we were also very cognizant of our audience. So we were hoping that planetary geologists might be able to pick this up and Keras had a great interface in Python. So we were we were hoping to make it as friendly and accessible to, to planetary geologists. But I think if you were to start from scratch today, I would probably recommend PyTorch.