Six thinking hats of Earth Observation

I have been reading Edward de Bono’s Six Thinking Hats – “Run better meetings, make faster decisions”. Having not come across this book before I have become increasingly drawn into this idea of the hats and their associated ways of thinking. The idea is that you put on one of these hats at a time to generate that type of thinking and then move to another hat / type of thinking to suit the situation.

The hats

White Hat – facts

Red Hat – feeling / emotions / gut feelings

Black hat – caution and risk

Yellow hat – positive and constructive

Green hat – creative thinking

Blue hat – control, organising

There is no order of using these hats, but it seems (to me at least) that the blue hat would be a good starting point to define a problem and a good ending point to summarise the findings.

Blue hat thinking

“Can high resolution EO data be more than just a basemap?” I think this would be a good question to explore with these hats.

Let me start with a bit of White hat thinking – some facts.

Red hat thinking (emotional)

  • Naps.Google? Google owning Terrabella, implies to me that they see a growing market for EO data. Could it be that it will be a complementary dataset to google maps? Are they be seeing this as valuable data? Google are well known for Moon shots. Is that a sign of a company that is restless and constantly wants to innovate? Does EO fit in with this thinking, of the world’s biggest information/knowledge system?
  • My gut feeling is that not all these planned launches will happen, but that there will be a fight for market dominance.
  • Imagery is changing (undoubtedly), esp in its temporal nature. We appear to be on the edge of every 3-5m square of the earth being imaged every day.
  • Public funded Earth Observation programs seem, to me, that they need to reach the public more
  • GPU technology appears to be making the high speed processing of high density data content more available to users, probably through cloud storage.
  • It feels like there is a change in the air from basemaps to Intelligence in pixels.

Black hat thinking (critical, caution, risk)

Oh no, not the negative black hat!

  • Haven’t we been here before? Earth Observation is not new (The Landsat program has been around for years)
  • Is there going to be a significant leap in understanding on how to use data and trust of the data is going to be there for us to rely upon? Who will drive that?
  • What are suppliers seeing that we as users are not seeing? Is this surge in data being driven by users or by suppliers? Is there a race to commercialise and take the market share and then try to grow the levels of activity in that market?
  • In the satellite market the breakdown is approximately 70% Telecommunications, 28% Positioning and 2% Earth Observation. How likely is that to change?

Yellow Hat (positive, constructive)

Enough black hat, let’s look on the bright side!

  • Big data is the thing these days; huge amounts of money and time is being spent collecting and analysing this data and Earth Observation seems to be just falling into our laps.
  • For the most part this data is relatively clean (ok, clouds… – go away black hat) and has been replicated for many years.
  • Complex referencing processes can be handled by a computer; users don’t need to be in front of heavy duty image remote sensing software.
  • The cloud takes away our storage problems
  • Companies are offering to store our data and allow us to run our algorithms at a seemingly low cost
  • The cost of all types of Earth Observation data has decreased; cost is not the barrier it used to be
  • Companies like Orbital Insights, Descartes labs and Timbr have appeared in the machine learning and EO sphere and have started getting some very interesting and exciting results.
  • Companies like Tomnod are using the crowd to derive solutions from satellite imagery

Green hat (creative thinking)

  • Let’s get thinking about the images and what could be done with them.
  • Could you “Information reference” an image? Could you click on an object and instantly be given a temporal view of that object? Could you be given the facts?
  • Could investors be supplied an automatic summation of the area/assets over time that could lead to project decisions?
  • Could mass migration patterns be analysed to foresee potential pinch points? Could this derive fact based policy? Could pseudo real-time events be better managed?

Red Hat thinking (emotional)

  • It feels like we will have access to more robust information; EO doesn’t have bias.
  • Budgets are tight in some of the core sectors at the moment: Oil and Gas, Mining, Government spending and defence spending. Maybe these markets are going to be harder to reach through a lack of investment in trial projects?

Black hat thinking

  • Only when value can be shown will the market gain traction
  • What are the commercial problems we are trying to address?
  1. Monitoring of assets & projects
  2. Modelling future trends patterns that may impact the business
  3. Responding as soon as possible to events that impact us
  4. Making better decisions on future growth areas
  5. Save money or make money

Blue hat thinking

What is missing?

  • Awareness both from the public and from business
  • Confidence that this is a real and measurable thing
  • The tipping point –what tipped GIS to flood over all sectors? Why is salesforce such a powerful tool? Why is YouTube so successful but Google Glass isn’t?
  • Mainstream adoption: do users need to know they are looking at Earth Observation data?

What have we got?

  • Faster computers, more data storage, more data
  • A race to commercialise space
  • Large government or pan governmental funding
  • A constant need for knowledge, a need to differentiate ourselves from others
  • Humans are good at looking for patterns!

Green Hat thinking

What can be done?

Should we all be moving away from a data driven age towards an information age?

  • Earth Observation needs to complement other information
  • The world’s largest information engine (Google) could be the vehicle for this information
  • EO data needs georeferencing but also information referencing; users might not even look at an image but rather search for information about irrigation patterns in California and get information back that has been derived from EO data
  • Companies should be able to make requests about seeing the movement of assets over time: “this boat was picked up by Pleisades on Saturday, Planet Doves on Sunday, WorldView-3 on Tuesday, Deimos-2 on Friday. It was or wasn’t in the expected location”. It is only when this information is available that EO could be used as a starting point to ask the question why is this happening?

Blue hat thinking

We need to be thinking of Earth Observation data more in terms of what, why, where and when as opposed to “that is a nice picture”. Users need to define what they are wishing to measure and EO might be one of the tools used to measure it. As with any technology a tipping point is often gained not by something that is unknown today but by a non-specialist doing the job they are specialist in and asking the right question. This is great but it could it be even better?

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