Sincere appreciation to my co-authors: @mchll-ln.bsky.social , Brefo Dwamena Yaw, Jade Boutot, Mary Kang , @drolnick.bsky.social .
@mila-quebec.bsky.social
#MachineLearning #ComputerVision #ClimateAction #RemoteSensing #aiforgood
Look for our work at #ICML2025!
Our work includes evaluating baseline AI algorithms. While results show promise for automated monitoring, they also highlight the task's complexity, inviting further research to enhance scalability & accuracy.
Dataset: zenodo.org/records/1374...
Preprint: arxiv.org/abs/2410.09032
It is aimed to empower environmental action using remote sensing by enabling the scalable identification of such wells, assisting experts to:
(1) Better estimate methane emissions and groundwater contamination.
(2) Prioritize plugging and remediation of wells.
To address this, we developed the Alberta Wells Dataset (AWD): the first large-scale benchmark for this critical challenge. AWD provides over 213,000 well instances (abandoned, suspended, active) in Alberta, Canada, with high-resolution, multi-spectral satellite imagery.
The undocumented nature of countless such wells creates profound environmental uncertainty. In Canada, for example, abandoned wells are the biggest source of uncertainty in methane emissions estimates.
I’m happy to share that our paper on detecting oil & gas wells with AI has been accepted to ICML 2025! Millions of such wells exist, leaking methane & other toxins. We show how remote sensing can help find them, using high-resolution, multi-spectral satellite imagery.
@icmlconf.bsky.social
Looking at ICLR submissions with the lowest score - What a work of art! 🧵
