Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning
This work addresses the need for efficient calibration of low-cost PM2.5 sensors, which is crucial for scalable air quality monitoring, though it is incremental as it builds on existing transfer learning techniques.
The paper tackles the problem of calibrating low-cost air pollution sensors with minimal co-deployment data by proposing transfer learning methods, finding that a Model-Agnostic Meta-Learning (MAML) approach outperforms other baselines in extensive experiments.
Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic- Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.