# Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery

arXiv: https://arxiv.org/abs/2410.09032 · HTML: https://mlsanity.com/paper/2410.09032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 52 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 26 of 26)

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- [METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping](https://mlsanity.com/paper/2207.11166/) (2022)
- [OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery](https://mlsanity.com/paper/2011.07227/) (2020)
- [OGInfra: Geolocating Oil & Gas Infrastructure using Remote Sensing based Active Fire Data](https://mlsanity.com/paper/2210.16924/) (2022)

## Abstract (arXiv)

Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the groundwater. Many of these locations are unknown, preventing the wells from being plugged and their polluting effects averted. Remote sensing is a relatively unexplored tool for pinpointing abandoned wells at scale. We introduce the first large-scale benchmark dataset for this problem, leveraging medium-resolution multi-spectral satellite imagery from Planet Labs. Our curated dataset comprises over 213,000 wells (abandoned, suspended, and active) from Alberta, a region with especially high well density, sourced from the Alberta Energy Regulator and verified by domain experts. We evaluate baseline algorithms for well detection and segmentation, showing the promise of computer vision approaches but also significant room for improvement.