METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
This work addresses the lack of public data for machine learning researchers and practitioners to map methane sources, which is crucial for mitigating global warming, but it is incremental as it primarily provides a new dataset.
The authors tackled the problem of automated methane source mapping by constructing a multi-sensor dataset (METER-ML) with 86,599 labeled images, and their best model achieved an area under the precision recall curve of 0.915 for concentrated animal feeding operations and 0.821 for oil refineries on a test set.
Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is necessary. Recent advancements with deep learning on remotely sensed imagery have the potential to identify the locations and characteristics of methane sources, but there is a substantial lack of publicly available data to enable machine learning researchers and practitioners to build automated mapping approaches. To help fill this gap, we construct a multi-sensor dataset called METER-ML containing 86,599 georeferenced NAIP, Sentinel-1, and Sentinel-2 images in the U.S. labeled for the presence or absence of methane source facilities including concentrated animal feeding operations, coal mines, landfills, natural gas processing plants, oil refineries and petroleum terminals, and wastewater treatment plants. We experiment with a variety of models that leverage different spatial resolutions, spatial footprints, image products, and spectral bands. We find that our best model achieves an area under the precision recall curve of 0.915 for identifying concentrated animal feeding operations and 0.821 for oil refineries and petroleum terminals on an expert-labeled test set, suggesting the potential for large-scale mapping. We make METER-ML freely available at https://stanfordmlgroup.github.io/projects/meter-ml/ to support future work on automated methane source mapping.