# Towards Methane Detection Onboard Satellites

## TL;DR

This paper presents a novel machine learning approach for onboard satellite methane detection that works effectively with unorthorectified data, reducing preprocessing needs and maintaining high performance.

## Contribution

It introduces UnorthoDOS, a dataset and method that bypasses traditional preprocessing, enabling efficient methane detection with comparable accuracy.

## Key findings

- ML models on unorthorectified data perform comparably to those on orthorectified data
- Models trained on orthorectified data outperform matched filter baseline
- Released datasets and code facilitate further research in satellite methane detection

## Abstract

Methane is a potent greenhouse gas and a major driver of climate change, making its timely detection critical for effective mitigation. Machine learning (ML) deployed onboard satellites can enable rapid detection while reducing downlink costs, supporting faster response systems. Conventional methane detection methods often rely on image processing techniques, such as orthorectification to correct geometric distortions and matched filters to enhance plume signals. We introduce a novel approach that bypasses these preprocessing steps by using \textit{unorthorectified} data (UnorthoDOS). We find that ML models trained on this dataset achieve performance comparable to those trained on orthorectified data. Moreover, we also train models on an orthorectified dataset, showing that they can outperform the matched filter baseline (mag1c). We release model checkpoints and two ML-ready datasets comprising orthorectified and unorthorectified hyperspectral images from the Earth Surface Mineral Dust Source Investigation (EMIT) sensor at https://huggingface.co/datasets/SpaceML/UnorthoDOS , along with code at https://github.com/spaceml-org/plume-hunter.