# Predicting air quality via multimodal AI and satellite imagery

arXiv: https://arxiv.org/abs/2211.00780 · HTML: https://mlsanity.com/paper/2211.00780/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space](https://mlsanity.com/paper/2108.13902/) (2021)

## Built on by (most cited first, 8 of 8)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop Yield Prediction](https://mlsanity.com/paper/2502.06062/) (2025)
- [Dense Air Pollution Estimation from Sparse in-situ Measurements and Satellite Data](https://mlsanity.com/paper/2504.17039/) (2025)
- [Air in Your Neighborhood: Fine-Grained AQI Forecasting Using Mobile Sensor Data](https://mlsanity.com/paper/2506.10332/) (2025)
- [CityAQVis: Integrated ML-Visualization Sandbox Tool for Pollutant Estimation in Urban Regions Using Multi-Source Data (Software Article)](https://mlsanity.com/paper/2510.18878/) (2025)
- [PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO2 and SO2 Using Satellite-Ground Data Fusion](https://mlsanity.com/paper/2604.03311/) (2026)
- [TinyNina: A Resource-Efficient Edge-AI Framework for Sustainable Air Quality Monitoring via Intra-Image Satellite Super-Resolution](https://mlsanity.com/paper/2604.04445/) (2026)

## Abstract (arXiv)

Climate change may be classified as the most important environmental problem that the Earth is currently facing, and affects all living species on Earth. Given that air-quality monitoring stations are typically ground-based their abilities to detect pollutant distributions are often restricted to wide areas. Satellites however have the potential for studying the atmosphere at large; the European Space Agency (ESA) Copernicus project satellite, "Sentinel-5P" is a newly launched satellite capable of measuring a variety of pollutant information with publicly available data outputs. This paper seeks to create a multi-modal machine learning model for predicting air-quality metrics where monitoring stations do not exist. The inputs of this model will include a fusion of ground measurements and satellite data with the goal of highlighting pollutant distribution and motivating change in societal and industrial behaviors. A new dataset of European pollution monitoring station measurements is created with features including textitaltitude, population, etc. from the ESA Copernicus project. This dataset is used to train a multi-modal ML model, Air Quality Network (AQNet) capable of fusing these various types of data sources to output predictions of various pollutants. These predictions are then aggregated to create an "air-quality index" that could be used to compare air quality over different regions. Three pollutants, NO2, O3, and PM10, are predicted successfully by AQNet and the network was found to be useful compared to a model only using satellite imagery. It was also found that the addition of supporting data improves predictions. When testing the developed AQNet on out-of-sample data of the UK and Ireland, we obtain satisfactory estimates though on average pollution metrics were roughly overestimated by around 20%.