Geodiversity

Radiant Earth, whose CEO Anne Hale Miglarese I was lucky enough to see speak at the RSPSoc conference last year, partnered with Amazon in order to provide more ‘geodiverse’ training data for machine learning models. I think this is timely, as the AI4EO paradigm sets in. The availability of Sentinel 2 Analysis Ready Data from s3, as well as the ability for partial reads of this data using gdal, is the preferred option vs. Google Earth Engine for me for geodevelopment, so I’m delighted on these continuing data releases. I’ve been reading about rastervision, and look forward to sinking my teeth into this data with that as a supporting tool to see what kind of learning can be done!

Geodiversity is required for reliable modelling (source)

Past Sentinel 2 data, there’s so much opportunity  to shift thinking on how to develop AI4EO models, extending to other metrics  such as air quality (for instance from Sentinel3 SLSTR).

Keep an eye on this space – I’ll do an jupyter notebook or similar exploring the data once I get the chance!

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Earth from space

The BBC have released the first of a documentary series focusing on Remote Sensing, and how it has changed/can teach us about out changing planet. It’s definitely a tough subject to fill whole episodes with, so the style is somewhat blended between satellite imagery, and storytelling on the ground, which makes for a very different kind of wildlife documentary experience.

I’m particularly curious as to how they produced the ‘superzooms’, which involve both zooming into, and out from, individual elephants in Africa to a continent wide view, as  they’re extremely well done. I’m a bit skeptical as to how much space cameras are involved in videoing shaolin monks, and am curious which satellites would even have the capability for this – maybe Vivid-i could capture a short video sequence, but the resolution wouldn’t really be high enough to discern individuals, and the recently defunct Worldview-4 would only be able to capture stills. Regardless, it’s really a well paced, emotional episode which I enjoyed immensely.

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Sample from Worldview-4, available here

The series continues next week with an episode on patterns – the dunes of Namibia are an area whose beauty I only really discovered through sentinel_bot and I’m looking forward to learning more!

 

SentinelBot upgraded

I’ve been on a webdev kick since starting a new job, and have recently upgraded SentinelBot as a result. It now filters snow scenes less often and can handle atmospherically corrected products – I’ll be updating the github repository, and will be writing a post about my current job soon, but for now feast your eyes on some Sentinel goodness 🙂

 

 

Predictions, predictions, predictions

I’ve just listened to the latest episode of Alastair and Andrew‘s podcast, scene from above, and the discussion section based around near-future predictions for the Earth Observation (EO) industry, as well as some of the discussion in the news section, was extremely interesting. I’m fully onboard the hype train for machine learning booming in EO, with Andrew seemingly somewhat skeptical.

Before I go into why I think that’s the case, I’ll mention Alastair speaks about a Voyager documentary, the Farthest (I’ve actually just noticed a big Irish producer, crossing the line was involved in production, wahay!). It sounds absolutely incredible, and will go on my watch list, but Alastair’s comments reminded me of an xkcd comic alluding to the fact that the edge of the solar system is difficult to define! I actually really enjoyed listening to their thoughts on Voyager in general, and would love to hear more discussion around the history of EO as well as wider planetary missions – every time I read and think about Corona, for example, I can’t help but be amazed.

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Voyager spacecraft (NASA)

 

One of the main predictions made within the main section of the podcast is that analysis ready data (ARD) will see wider use and release by data providers. We have seen a move towards sentinel 2 ARD and planet have recently released their atmospherically corrected surface reflectance product, I would hope this is an indication that this is quite well developed already!

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A figure from Planet’s surface reflectance white paper (source)

On the machine learning (ML) front, I attended a google earth engine workshop at the beginning of this year, and having had fruitful discussions with the host on the project’s directions, I think the iron is hot for ML and the hype justified. In particular, the host spoke about the team preparing tensor flow integration into the platform in time for AGU next year. Having been lucky enough to participate in (albeit not at a competitive level) the planet kaggle competition for classifying image excerpts into one or more classes last year, I have a decent idea of just why there has been a frenzy of research surrounding convolutional neural networks (CNNs) in the computer vision community, and I’m surprised that they haven’t appeared more in EO research.

While Andrew notes that supervised and unsupervised classification has been around and used for decades, the difference between those and deep-learned information is like night and day in my opinion. The competition, past the task presented, gave me a look into how neural networks are transforming image analysis, and how recurrent CNNs on massive scales could be leveraged in an environmental context for things like linking phenological mapping to data which might provide reasons as to why a change is happening with spatial context. Object-based analysis is unparalleled for applications like this, and CNNs are now so easy to use and much better at handling massive data sets than previous methods. Computer scientists are poised to integrate more and more with the EO community as higher resolution data becomes available, and so I feel like when high temporal and spatial resolution open data becomes available multi-disciplinary research will really kick off. Infact, I put together a starter ipython notebook for bird identification, showing just how easy it is using a pre-trained CNN for this application, albeit not with EO data.

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Example plot from ipython notebook

This leads to a prediction of my own – as more imaging scientists move into EO, Unmanned aerial vehicle (UAV) and satellite data will need to be better integrated. Currently, there are a raft of problems linking data collected from consumer level cameras onboard UAVs to satellite data, not least of which is radiometric normalization. The demand for higher resolution data from the deep learning end of the community will lead to new standards being introduced for how UAV data is collected and metadata stored (shameless plug). EO platforms will begin to integrate publicly collected UAV data and satellite researchers will begin to collaborate with computer scientists using nearer earth images. We will then see satellites being used as an early warning systems and UAV missions automatically launched off the back of satellite derived information in a range of new applications.

This isn’t a particularly insightful prediction, but one which continuously hasn’t really been addressed. I’m always surprised as to how infrequently satellite and UAV data are used in tandem, but I’m hoping this will change!

That’s all for now, look for my Google Earth Engine blog coming next week, I was blown away by the product and definitely need to do a separate post on it 🙂

Scene from above

I’ve been severely neglecting my blog on account of focusing on writing up my PhD project as well as being sick (don’t underestimate the pain of getting your tonsils out as an adult!).

I wanted to write up a decent post for my 100th entry, but have subsequently realised it’s lead to me posting nothing for the last couple of months! I have a plan for a good entry coming up, though will need to find the time to put it together.

In the meantime, I picked up that Alistair Graham (geoger), who gave a talk at the conference I ran this year, and Andrew Cutts, who I have never met, though I remember worked through the straightforward openCV GUI demo from his website which I thought was great, have started a podcast, scene from above.

Science communication is tricky at the best of times, so I’m excited they’re giving this style of delivery a crack. The demo episode discusses Sentinel 5p and the larger scope of the sentinel project, remap’s webapp and cloud computing more generally, and the launch of a Moroccan satellite.

I think the discussion of the webapp was my favorite part. I appreciated Alistair’s humility in admitting that maybe he was approaching interaction with data from a point of view that was somewhat outdated, as he seems (as am I!) skeptical of the benefits of a sleek interface. Admittedly the app isn’t designed with me or others in the RS community in mind, but I can’t see it being used much in it’s current iteration.

Thinking of my ornithologist friends currently in PhDs/postdocs who would be the target audience for an app like this, they would almost definitely look at it for an hour or two with interest, and never think to use it again. Having consistently tried to get them interested in RS and accurate mapping, the tools need to be unbelievably simple to get people to consider using them seeing as so much of other scientists time is dedicated to learning specialist knowledge and general computing skills. It’s one of the many challenges of interdisciplinary work in science!

I’m looking forward to the next episode of the podcast, and hope a forum opens up for discussion online as I think I’d have something to contribute, and would love to hear other people’s opinions on these ideas!

Keep an eye out for a longer update soon 🙂

RSPSoc Annual Conference

I had a great time at the RSPSoc conference yesterday, and very much enjoyed catching up with the some of the people I made friends with at Wavelength this year – this is a short entry to just make available the slides of both Mike (supervisor) and myself, who’s primary focus was on image quality in photogrammetric work. Unfortunately I think I filled my slides a little too much and probably could have put in about half the content, but somehow couldn’t stop adding plots from the beautiful seaborn library, lesson learned!

Link to Mike slides

Link to my slides

Looking forward to writing a blog on RAW – JPEG conversions very soon, check the undemosaiced sneak preview below 😉

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Joypy

Not one to miss a fad in data visualisation, I noticed joyplots getting a lot of attention over at reddit’s dataisbeautiful subreddit and have given a go at producing some myself – I’m hoping to integrate them into a talk I’m giving this Wednesday as part of the RSPSoc‘s annual conference, and am hoping they make enough sense to include.

I’m tinkering with the joypy library, a set of scripts whose sole purpose is to produce these types of plots, built ontop of the excellent (and frequently used by myself) seaborn plotting library.

For now, I need to get of the fad wagon and keep on writing!

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A sample joyplot I’ve produced.