Portrait of Pratinav Seth

Pratinav Seth

seth.pratinav@gmail.com
Collaborating researcher
Supervisor
David Rolnick
Research Topics
Climate Change AI
Computer Vision
Deep Learning
Medical Machine Learning
Satellite Imagery
Trustworthy AI
Website
Google Scholar
GitHub

Publications

Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Pratinav Seth
Michelle Lin
BREFO DWAMENA YAW
Jade Boutot
Mary Kang
David Rolnick
Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the gr… (see more)oundwater. 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.
2024-10-11
ArXiv (preprint)
arxiv.org
arxiv.org
Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Pratinav Seth
Michelle Lin
BREFO DWAMENA YAW
Jade Boutot
Mary Kang
David Rolnick
Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the gr… (see more)oundwater. 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.
2024-10-11
ArXiv (preprint)
arxiv.org
arxiv.org