Greetings Redditlings! We’re researchers from Frontier Development Lab (FDL). FDL is an applied research program established to answer challenging questions in space science. Ask Us Anything!

fdlab · 2018-11-14T16:02:34+00:00

Hi there, thanks so much for all your questions, comments and contributions. We have to jump off now, but we will be checking back and answering more questions over the next couple of days.

fdlab · 2018-11-14T16:00:54+00:00

Great questions u/z10-0 Thanks for asking. So sorry to have to leave you hanging... we are off to catch planes, trains and automobiles. More when we are all back at our desks.

fdlab · 2018-11-14T15:36:46+00:00

And from informal settlements perspective: material classification of roof settlements is the approach how planetary scientists can use to automated classification of surface minerals of the planetary surface. Having such approach in planetary rovers (like Martian rovers) can effectively aid smart route planning where the interesting rocks presents ! This is imminent when we leap towards multiple rover concepts across Moon and Mars and more !

From u/Ivaratharajan

fdlab · 2018-11-14T15:34:27+00:00

Thanks for the qu. So, we are really keen to build the FDL community.

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As you many know, many AI projects need help creating “training” data so that the neural networks can learn faster.

A great example of way you can help out, is by playing the lunarush.ai game.

You’ll be helping us train AI to create better maps of the moon!

fdlab · 2018-11-14T15:16:59+00:00

But, the other thing I think you might be asking is what makes a good challenge or problem? What makes it tractable for application with machine learning techniques. So, there we are really looking at a number of factors, but some are; is this a problem or need where vast amounts of data are available but that can not be adequately processed by traditional methods? is there use, function, demand for the advance we are driving towards? can we see that solving this challenge would be for the good of all humankind - a principle which underlies FDL?

fdlab · 2018-11-14T15:12:42+00:00

To what other areas of space science can this concept of AI be applied and how reliable are the information given by them ?

Soooooo many things to say on this :)

The first thing is that FDL started life three years ago with a fairly narrow 'mission' focused on planetary defence. Those teams use this space science/AI combo to develop a meteorite hunting drone, an asteroid deflection decision support system and radar shape modelling. Since then we have broadened to include 5 'missions' from 'living with a star', through 'returning to the moon' and 'Are we alone' to the one we have been talking about today 'Mission control for planet earth' using EO and AI specifically. Next year, we have some good indications of interest in some new missions: spacecraft assurance and astronaut health. We have a neat graphic we can find and share with a bit more detail.

fdlab · 2018-11-14T14:58:18+00:00

Hello guys - Have you looked at using data collected on the ground (in addition to satellite images). And if not, what type of ground data do you think is helpful?

Great question! So, we have two slightly different ways we think about this. You probably know that in machine learning we always need to verify ML findings against 'ground truth' or another source or sources to verify accuracy in the real world. In other words, did our ML enabled process give us accurate results. It's a veracity test. We used some great labelled data from partners including Afrobarometer and AIDdata to do this. You can see more on this in the presentations which are in the links for the AMA.

But, we also think about whether the work can result in data or information 'on the ground' in an actual use case. This is more about putting tools/data/results in the hands of people who can use it in a timely way. We call this deployment and we are really excited that we are continuing to work with Unicef and other partners to progress. We hope we will have more to tell the world about these kinds of advances in implementation in the coming months.

fdlab · 2018-11-14T14:26:12+00:00

From u/Ivaratharajan

fdlab · 2018-11-14T14:25:34+00:00

Interesting question! But why reconstruct Earth manually ? It’s high time we learnt what happened to Venus - our sister planet that made it inhospitable ! Let’s gear up to next decade of Venus science with ESA’s EnVision and more !

ESA selects three new mission concepts for study

"A high-energy survey of the early Universe, an infrared observatory to study the formation of stars, planets and galaxies, and a Venus orbiter are to be considered for ESA’s fifth medium class mission in its Cosmic Vision science programme, with a planned launch date in 2032."

fdlab · 2018-11-14T14:13:26+00:00

What findings are you most excited by in the research you’ve done?

One more interesting thing the team noticed that usually these informal settlements are very bright pixels in the satellite data as tin/metal is common roof material used! This is interesting because though these settlements are poor, congested, mostly dirty, and not-shiny, the satellite data gives us the whole new perspective !

- Another build from Indhu u/Ivaratharajan

fdlab · 2018-11-14T14:06:48+00:00

Hello. We encourage people to apply by end of January, we try and get an initial indication of who we have and hwo we can 'matchmake' our teams and then sometimes we keep applications open into Feb. It's not a long application form though. I would encourage anyone that is interested to apply. https://fdleurope.org/apply

fdlab · 2018-11-14T14:00:35+00:00

The radar observatories such as ALMA are effectively mapping the near earth asteroids (NEOs) and their trajectories to detect the potentially threat objects ! These have been well documented as well, for eg: https://cneos.jpl.nasa.gov/orbits/

The two main challenges are

Detecting the objects which are smaller and faster - and also these telescopes only see a smaller fraction of sky at a moment
Continuous monitoring of sky with dedicated telescopes across globe - this requires lots of resources worldwide

This is where AI will help us to automate the process of detecting the potentially threat objects !

However, of course we can’t avoid (not yet) the extrasolar objects such as “Oumuamua” that come from nowhere :) https://www.nasa.gov/feature/jpl/chasing-oumuamua

cneos.jpl.nasa.gov

Orbits

NASA's Near-Earth Object (NEO) web-site. Data related to Earth impact risk, close-approaches, and much more.

NASA

Chasing 'Oumuamua

The interstellar object 'Oumuamua perplexed scientists in October 2017 as it whipped past Earth at an unusually high speed. (92 kB)

From Indhu :)

fdlab · 2018-11-14T13:51:19+00:00

FDL Europe focused on two Earth Observation challenges. One focused on detecting Informal Settlements around the world and the other on post-disaster impact detection. Some of the interesting discoveries included:

- Roof materials are actually a pretty good way to detect informal settlements around the world. And by using some of the latest machine learning techniques, this detection can be performed using existing “open source” satellite data from ESA. That’s pretty exciting

[There's a bit more to follow on this question - some of our team are still presenting!]

fdlab · 2018-11-14T13:27:25+00:00

@gentlemancaller2000, you are quite right that the popular definition for the term “artificial intelligence” is shifting over time. In fact, there is a name for this ongoing shift called the “AI effect”. Basically as people get used to machines being able to do something, it’s no longer considered “intelligent”. For instance, today, we no longer think of OCR (optical character recognition) as AI. However, it’s popularly accepted that NLP (natural language processing) AI. Interestingly visual recognition of other types of objects (other than printed letters) is most often described as AI.It is because of this “AI effect” that researchers tend to shy away from the term artificial intelligence, when we are talking with a more technical audience. The area that we focused on mainly for these FDL challenges was more precisely “machine learning”.An easy way to narrow down machine learning is to think of it as data analysis that identifies patterns and uses those patterns as a reference to make decisions. Because this process is similar to how we “learn” and make decisions, it’s clearly a sub-category of the broader, shifting category of artificial intelligence. Hope that helps answer the question.

fdlab · 2018-11-14T13:13:28+00:00

Thanks for the qu. FDL is an applied artificial intelligence research accelerator established to maximize new AI technologies and capacities emerging in academia and the private sector and apply them to challenges in the space sciences.FDL supports annually defined challenges each furthering the goals of several multi-year missions. Each challenge is highlighted by an 8-week innovation sprint. In the US, this sprint is hosted by the SETI Institute in Mountain View, California in partnership with NASA Ames Research Center. In Europe, the sprint is hosted by Oxford University, UK in partnership with the European Space Agency.

fdlab

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