Clean Technology Meets Data Science: News and Articles

  A big thank you to everyone who joined us last weekend for a lively and interesting discussion on data engineering and how to build prototypes that access satellite imagery using Google Earth Engine and Python.   It’s always fun to talk about satellites, imagery and how to get things to work in many different clean technology sectors - agriculture, water, energy, climate and disaster management among them.     Today, let’s talk about one sector that doesn’t get as much attention - forestry.   If you heard the the words forests and satellite imagery in one sentence, what comes to your mind? Deforestation? Reforestation? Wildfires? All three?   Managing our forests sustainably is key to protecting the environment in so many different ways - forests have a huge impact on climate, on ecosystem services and on the livelihoods of communities that rely on them. However, the challenge is that most forests are hard to access and data is often difficult to verify o...

  In June this year,   we had a lively discussion and online workshop on remote sensing data   and how monitoring processes occurring on the Earth was why the Landsat satellite program was launched in the 1970s - a program that’s still running today.     But here’s an interesting question that came up in our conversation - since water, agriculture, energy and other clean tech sectors have been using remote sensing data for such a long time - what is so different now?     To answer that question, let’s first talk about how satellite data is used in clean technology. The sectors where satellite data, and data science in general, are widely used both commercially and in research and development are agriculture, energy, water, climate and disaster management.   So, what are the different uses of satellite data in each of these sectors?   Let’s take agriculture first.   Researchers and scientists have been using satellite data since the 1970s...

  Last week we talked about how the market in clean technology and data science   is already in the multi-billion dollar range and is headed to the multi-trillion dollar space in the next decade or so. However, one of the challenges that analysts highlighted was the lack of professionals who have sufficient expertise in both clean technology and data science. So today, let’s take a look at what’s happening in educating professionals in this exciting, new field as well as the kind of skills that are needed.   Most of the traditional college and university programs haven’t yet caught up with the demand for professionals at this intersection of specialities - although they are getting there! While many universities and colleges have created data science degrees - these usually focus on the problems that are faced by the high-tech and internet sectors. The graduates from these programs usually have a pretty solid understanding of coding, algorithms including machine learning,...

  If you were to ask people why they’re interested in applying data science in clean technology - the chances are that you’ll come across three answers. 1) They want to make a difference to the planet and help people 2) They think it’s cool technology and want to be at the forefront of innovation and 3) They’ve heard it’s a hot and upcoming field with lots of jobs and opportunities and want to get in at the ground level.   Now, one and two are both pretty obvious - but what about the third reason? Is the intersection of clean technology and data science really such a growing field?     To answer that question, let’s take a look at some numbers. Now, about five years ago, when the field was in its infancy, there was a lot of speculation about the field being anywhere from a   multi-billion dollar market to a multi-trillion dollar market . How did those estimates hold up, now as we look at what the next five years may bring?   As it turns out, the estimates h...

  One of the first questions I get about workshops on this topic is - why do we need to talk about machine learning again? There are tons of online courses available already, lots of free material on the web and libraries in Python that are easy enough to get started with. So, why look at this stuff once more? Why not just point us to the best existing resources and let us get on with it?   And the answer is - yes, there are lots of excellent resources (free and paid) on machine learning and yes, we’ll have a list of those resources available for additional reference. But, and this is a big but - many of these resources are targeted to problems faced in the high-tech sector, where the data and types of problems are very different. When we’re solving problems in clean technology, the kind of data we have and the questions we’re faced with are often quite different. That means that machine learning algorithms have to be adapted to work in our sector - and the way they get adapte...

  When faced with a question about agriculture, water, energy, air or another clean technology system, how do you decide to model it? Do you dive down deep into the subject matter and try and figure out what would work? Or do you go wide, look at the interactions between different systems and see how different types of data and models can be combined? Or do you do a mix of both?     Like many other challenges in the data science and clean technology fields, it really depends on the question you’re trying to answer - and the data that are available. If the question you’re trying to answer has a relatively well defined process and sufficient data - then it makes sense to start by diving deep into the subject and looking at different processes and interactions within a relatively narrow field. For example, let’s say that we’re trying to understand the chemical interactions in a water treatment plant process and if there’s a problem with how effectively the treatment process ...

  It’s hard to believe that July is already here - what a year it’s been so far! When the pandemic started we were just beginning to launch our series of online courses and workshops to a small community of professionals who were interested in applying data science to solving the problems facing our planet. Our community has been growing every month that we’ve been doing these courses and workshops - and this month, we're doing a workshop on a topic that many of our members have asked us about -   Machine Learning in Clean Technology . If you’re interested in learning about machine learning in clean technology - the problems and use cases in clean technology, what algorithms are deployed most often, how machine learning in clean tech differs from other common applications in high tech, and how to get started - join us on  Sunday, July 19th at 11am -12.30 pm Pacific Time.    You can sign up for the workshop here or on our courses page .  So, h...

  The geospatial and remote sensing sector has exploded in the last five years and is poised for even more growth over the next decade. In the initial years, companies in this sector were pretty niche - with customers that were primarily in government and the military. And then, as computing power became cheaper, satellite and drone costs decreased to a fraction of what they used to cost and other uses of remote sensing and spatial data were discovered, the sector exploded with new startups entering the field and larger companies expanding their offerings.   In fact, the market in geospatial data and remote sensing is expected to double from $53 billion in 2019 to $110 -$134 billion by 2025 at an annualized growth rate of ~15%.     It’s a global market, with the United States being the largest player so far with 40% of the market, closely followed by the European Union. The rest of the world is also growing rapidly, with India, China and Argentina being the leading m...