Publication
Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
Daniel Ochs; Karsten Wiertz; Sebastian Bußmann; Kristian Kersting; Devendra Singh Dhami
In: Remote Sensing, Vol. 16, No. 2, Pages 1-13, MDPI, 2024.
Abstract
Natural gas pipelines represent a critical infrastructure for most countries and thus their
safety is of paramount importance. To report potential risks along pipelines, several steps are taken
such as manual inspection and helicopter flights; however, these solutions are expensive and the
flights are environmentally unfriendly. Deep learning has demonstrated considerable potential in
handling a number of tasks in recent years as models rely on huge datasets to learn a specific task.
With the increasing number of satellites orbiting the Earth, remote sensing data have become widely
available, thus paving the way for automated pipeline monitoring via deep learning. This can result
in effective risk detection, thereby reducing monitoring costs while being more precise and accurate.
A major hindrance here is the low resolution of images obtained from the satellites, which makes
it difficult to detect smaller changes. To this end, we propose to use transformers trained with
low-resolution images in a change detection setting to detect pipeline risks. We collect PlanetScope
satellite imagery (3 m resolution) that captures certain risks associated with the pipelines and present
how we collected the data. Furthermore, we compare various state-of-the-art models, among which
ChangeFormer, a transformer architecture for change detection, achieves the best performance with a
70% F1 score. As part of our evaluation, we discuss the specific performance requirements in pipeline
monitoring and show how the model’s predictions can be shifted accordingly during training.