objective , which they accomplished last year , involved replicating the results from that proof of concept with public data available through Google Earth Engine , a cloud-computing platform that stores large satellite-image data sets . “ We chose to start with public data because it helps us establish a baseline for what we can accomplish with minimum cost , and there are a variety of different satellite collections available on the platform ,” Mitchell explains .
Their focus now , Mitchell says , is on developing a general monitoring model and then using it “ to make as many predictions as we can .” They hope , for example , to be able to estimate the total annual carbon emissions of individual plants , and they ’ d like to eventually interpolate between predictions to create “ granular profiles ” of each facility .
AIMING HIGH As of now , according to Isabella Soldner-Rembold , a data scientist on the Carbon Tracker team , the program is using data from just a handful of satellites . Different satellites have different orbits , she explains , and the imagery each one is able to provide depends on its location at different times of day .
The coalition doesn ’ t own the satellites in its network , but instead relies on the data feeds from commercial and government satellites like the European Space Agency ’ s Copernicus Sentinel-2 . Such satellites are preferable to anything they might launch independently because many have been collecting data for years . “ When training machine learning algorithms , more training data generally increases the accuracy of the model ,” Soldner-Rembold says . The initiative is still in the research and development phase , but the team hopes to release data as early as fall 2020 , and to eventually — perhaps within two years — have data from enough satellites to enable round-the-clock monitoring “ of every power plant across the entire globe .”
Her current focus , Mitchell says , involves using machine learning techniques with Earth Engine to process not only their satellite data but also “ ground truth ” data from power plants themselves . The coalition ’ s AI algorithms , she explains , can analyze a range of indicators of power plant emissions , whether it ’ s a thermal infrared image showing signs of heat or certain colors in an image suggesting the presence of smoke . This information , in turn , can help them determine whether a facility is a “ baseload plant ” that operates most of the time , or if it ’ s more likely a “ peaker plant ” that only generates electricity during very high demand . ( Peaker plants are typically high emitters of CO 2 and other pollutants .) “ This data is helping us see things we could never see before , and to really understand the actual impact ” of any given power plant at any time , she says .
Looking ahead , Mitchell and Soldner-Rembold believe the data their organizations make public will be of great interest to a wide variety of stakeholders . Investment firms , for instance , may use it to better understand the financial implications of climate change and to steer their clients away from high-cost polluters . Activists may turn to the same information to lobby politicians for policy changes , and governments keen on meeting the goals of the Paris Agreement may use it to drive their countries toward a cleaner future .
In the end , Mitchell predicts , the people it will benefit most “ are those who are willing and really want to initiate change , but right now are unable to because they don ’ t have the resources .” If the project helps to turn the tide , “ I think we would call it a success .” ■
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