MMAQ: A Multi-Modal Self-Supervised Approach For Estimating Air Quality From Remote Sensing Data

Air quality intensifies climate change through global pollution, particularly affecting lower and middle-income countries that lack local ground pollutant monitoring networks. While atmospheric pollutant satellite measurements offer a broad view, their coarse spatial resolution limits detailed air pollution insights, resulting in unmonitored regions and information gaps. Furthermore, satellites generate extensive data that is often challenging to directly correlate with ground air pollution stations. To overcome this, we propose a multimodal self-supervised approach that learns from diverse satellite sources for air pollution monitoring. More specifically, aside from incorporating a combination of multi-spectral and spectral modalities (Sentinel-2 & 5P products), we also leverage tabular land cover data. Their integration into multimodal self-supervised learning is highlighted, employing a novel augmentation scheme that results in more resilient embeddings. The proposed approach integrates a self-supervised redundancy reduction loss in a multi-modal fashion, capturing both inter-modal and intra-modal correspondences. Furthermore, an adaptive loss weighting mechanism is introduced to effectively combine different multi-modal losses. Our approach’s efficacy is showcased in the air pollution prediction task, exhibiting a noteworthy improvement of up to 17% compared to existing methods. Furthermore, in our experiments, the applicability of our approach in other environmental tasks is also exhibited.