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

Pratinav Seth, Michelle Lin, Brefo Dwamena Yaw, Jade Boutot, Mary Kang, David Rolnick
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:54030-54057, 2025.

Abstract

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 high-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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-seth25a, title = {Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery}, author = {Seth, Pratinav and Lin, Michelle and Yaw, Brefo Dwamena and Boutot, Jade and Kang, Mary and Rolnick, David}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {54030--54057}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/seth25a/seth25a.pdf}, url = {https://proceedings.mlr.press/v267/seth25a.html}, abstract = {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 high-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.} }
Endnote
%0 Conference Paper %T Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery %A Pratinav Seth %A Michelle Lin %A Brefo Dwamena Yaw %A Jade Boutot %A Mary Kang %A David Rolnick %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-seth25a %I PMLR %P 54030--54057 %U https://proceedings.mlr.press/v267/seth25a.html %V 267 %X 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 high-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.
APA
Seth, P., Lin, M., Yaw, B.D., Boutot, J., Kang, M. & Rolnick, D.. (2025). Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:54030-54057 Available from https://proceedings.mlr.press/v267/seth25a.html.

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