We had a great time hosting our "Getting started with Data Science for clean technology professionals" webinar - a big thank you to everyone who registered and asked all those interesting questions! If you're interested in getting the recording, it's now available for download on our website. And now, back to our regular posts on how data science is used in different clean tech fields! There's been a lot of news about the wildfires in the Amazon and the consequences for the planet, so let's talk about wildfire detection and how it's done. Wildfires, and in particular the Amazon fires, are detected using data from a wide range of satellites - NASA's array, the EU's Copernicus, Brazil's Terra satellites, Japan's Himawari-8, and CubeSats among others. But what exactly do these satellites see and how can you identify a wildfire from the data? Typically, satellites carry multi-spectral cameras or sensors on board. As the satellite passes o
Showing posts from August, 2019
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We've been getting a lot of questions recently from folks who are already working in clean tech fields - water, energy, agriculture, environmental consulting, climate change - about data science and what you need to get started. Some of the questions that we get asked a lot: Is all this AI and data science stuff hype? Is it really useful? How is data science different from the traditional statistical methods that we've used? I'm confused about what a data scientist does? Is that different from a data analyst? I can see the potential, but there I don't know where to start and which options are most useful to me at this stage. If this sounds like something you've been thinking, join us for a free webinar this Friday, August 16th at 11.30 am Pacific Time!
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"If you can't measure it, can you fix it?" One of the greatest challenges faced by almost everyone working in a clean technology field - water, agriculture, energy, climate, forestry, wildlife, soils, corporate sustainability, smart cities - is the challenge of monitoring. At its essence,this is the challenge of what needs to be measured, how often and how accurately can it be done . Traditional methods of monitoring have involved sensors (of different levels of accuracy) placed in specific locations and the data removed and processed off-site by engineers and field analysts at specific time intervals. This is a time-consuming process, with data that isn't as frequent or as spatially dense or with as many parameters as decision makers and scientists would like - but, until recently that's been the best that we've had. The advent of smartphones, high-frequency and high resolution satellite data and the whole Internet of Things (IoT) is changing this parad