Public repository for "Deep learning for detecting and characterizing oil and gas well pads in satellite imagery."
This repository contains:
- Datasets used to train well pad and storage tank detection models (
data/training/datasets). We note that we are unable to redistribute the satellite imagery used to train the models in this study due to data licensing. - Well pad and storage tank deployment detections across the entire Permian and Denver basins generated in this study (
data/deployment). - Outputs from experiments on training dataset test splits (
data/training/results), and evaluation code (code/eval_test.py) for replicating performance metrics (i.e., Tables 1 and 4 in the paper). - Evaluation code (
code/eval_deployment.py) for comparing deployment detections to reported HIFLD well pad data (i.e. Fig. 2 in the paper).
Results from 3. and 4. can be verified by running the evaluation code using the command python code/eval_all.py.
Code for training the models in the study may be made available upon request to the corresponding author.