# Estimation of Air Pollution with Remote Sensing Data: Revealing Greenhouse Gas Emissions from Space

arXiv: https://arxiv.org/abs/2108.13902 · HTML: https://mlsanity.com/paper/2108.13902/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Characterization of Industrial Smoke Plumes from Remote Sensing Data](https://mlsanity.com/paper/2011.11344/) (2020)

## Built on by (most cited first, 5 of 5)

- [Predicting air quality via multimodal AI and satellite imagery](https://mlsanity.com/paper/2211.00780/) (2022)
- [CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation](https://mlsanity.com/paper/2501.15389/) (2025)
- [GeoViT: A Versatile Vision Transformer Architecture for Geospatial Image Analysis](https://mlsanity.com/paper/2311.14301/) (2023)
- [GeoFormer: A Vision and Sequence Transformer-based Approach for Greenhouse Gas Monitoring](https://mlsanity.com/paper/2402.07164/) (2024)
- [Dense Air Pollution Estimation from Sparse in-situ Measurements and Satellite Data](https://mlsanity.com/paper/2504.17039/) (2025)

## Abstract (arXiv)

Air pollution is a major driver of climate change. Anthropogenic emissions from the burning of fossil fuels for transportation and power generation emit large amounts of problematic air pollutants, including Greenhouse Gases (GHGs). Despite the importance of limiting GHG emissions to mitigate climate change, detailed information about the spatial and temporal distribution of GHG and other air pollutants is difficult to obtain. Existing models for surface-level air pollution rely on extensive land-use datasets which are often locally restricted and temporally static. This work proposes a deep learning approach for the prediction of ambient air pollution that only relies on remote sensing data that is globally available and frequently updated. Combining optical satellite imagery with satellite-based atmospheric column density air pollution measurements enables the scaling of air pollution estimates (in this case NO2) to high spatial resolution (up to ~10m) at arbitrary locations and adds a temporal component to these estimates. The proposed model performs with high accuracy when evaluated against air quality measurements from ground stations (mean absolute error <6~μ g/m3). Our results enable the identification and temporal monitoring of major sources of air pollution and GHGs.