Ochs, Daniel ; Wiertz, Karsten ; Bußmann, Sebastian ; Kersting, Kristian ; Dhami, Devendra Singh (2024)
Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images.
In: Remote Sensing, 2024, 16 (2)
doi: 10.26083/tuprints-00027169
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Ochs, Daniel ; Wiertz, Karsten ; Bußmann, Sebastian ; Kersting, Kristian ; Dhami, Devendra Singh |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images |
| Sprache: | Englisch |
| Publikationsjahr: | 13 Mai 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 10 Januar 2024 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Remote Sensing |
| Jahrgang/Volume einer Zeitschrift: | 16 |
| (Heft-)Nummer: | 2 |
| Kollation: | 13 Seiten |
| DOI: | 10.26083/tuprints-00027169 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27169 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | transformer, PlanetScope, pipeline monitoring, change detection |
| ID-Nummer: | Artikel-ID: 266 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-271698 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Künstliche Intelligenz und Maschinelles Lernen Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) Zentrale Einrichtungen > hessian.AI - Hessisches Zentrum für Künstliche Intelligenz |
| Hinterlegungsdatum: | 13 Mai 2024 12:39 |
| Letzte Änderung: | 16 Mai 2024 14:49 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
- Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images. (deposited 13 Mai 2024 12:39) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum