EcoSat: Detecting Greenhouse Gas Emissions Worldwide using Satellite Imagery and Deep Learning

Greenhouse gas emissions heavily contribute to global warming, causing devastating impacts on society. Climate change has increased the number and size of wildfires along with extending wildfire seasons, which only further damages the environment. A study of the 196 countries in the Paris Agreement found most underreporting their emissions; for example, Malaysia emitted 422 million tons of emissions in 2016 but only reported 81 million. Since tracking is done at a country level, each country uses different tracking methods and can falsify its data. With accurate and unbiased emissions tracking, setting climate goals and determining the effectiveness of solutions are possible.There is a need for an unbiased, quantitative, real-time, and consistent compliance solution for emissions tracking. 30% of emissions come from facilities, mostly coal power plants emitting smoke plumes. Satellite imagery covers all facilities in the world at high frequency. Meanwhile, the US Environmental Protection Agency (EPA) accurately gathers emissions data from over 8,000 US facilities. By taking facilities from this data, collecting satellite imagery of the facilities using SentinelHub, segmenting the smoke plumes from the images using my own YoloV8 segmentation model, and extracting features from the smoke plumes, I have created a deep-learning regression model correlating the smoke plumes to the actual emissions, achieving an 8% Mean Absolute Percentage Error (MAPE) on my testing dataset, and proving the feasibility of accurately tracking facility emissions worldwide in real-time using satellite imagery and deep-learning AI to keep countries accountable.The goal of my project is to accurately estimate greenhouse gas emissions from different facilities (power plants, waste facilities, etc.) using high frequency, publicly available 12-channel satellite imagery, which enables unbiased, accurate tracking of emissions worldwide without relying on local agencies.