The World Health Organization estimates that air pollution causes more than 2.2 million deaths
in the Asia Pacific region every year. Air pollution from fine particulate matter (PM 10 or 2.5) increases the risks of heart and lung diseases, stroke, and cancers, along with other diseases.
While there have been growing efforts to monitor air quality in recent years, ground monitoring stations–especially in low and middle-income countries–remain sparse due to deployment costs and complexity.
As part of Thinking Machines’ and UNICEF’s AI4D (Artificial Intelligence for Development) Research Bank
program, we tested the feasibility of training a machine learning (ML) model on remote sensing data to estimate particulate matter PM2.5. Our exploratory and foundational research focused on Thailand, one of the countries in Southeast Asia that practice open burning of agricultural waste during the post-harvest season. Our work is fully open-sourced, including the following components: