Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images

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 a...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-01, Vol.16 (2), p.266
Main Authors: Ochs, Daniel, Wiertz, Karsten, Bußmann, Sebastian, Kersting, Kristian, Dhami, Devendra Singh
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Change detection
Comparative analysis
Construction
Critical infrastructure
Datasets
Deep learning
Gas pipelines
Helicopters
Households
Image resolution
Machine learning
Monitoring
Natural gas
Pipe lines
pipeline monitoring
Pipelines
PlanetScope
Remote sensing
Risk assessment
Satellite imagery
Satellite imaging
Satellites
transformer
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16020266