elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Detecting Pipeline Pathways in Landsat 5 Satellite Images With Deep Learning

Dasenbrock, Jan und Pluta, Adam und Zech, Matthias und Medjroubi, Wided (2021) Detecting Pipeline Pathways in Landsat 5 Satellite Images With Deep Learning. Energies. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/en14185642. ISSN 1996-1073.

[img] PDF - Verlagsversion (veröffentlichte Fassung)
2MB

Kurzfassung

Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model's capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany.

elib-URL des Eintrags:https://elib.dlr.de/144069/
Dokumentart:Zeitschriftenbeitrag
Titel:Detecting Pipeline Pathways in Landsat 5 Satellite Images With Deep Learning
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Dasenbrock, Janjan.dasenbrock (at) dlr.dehttps://orcid.org/0000-0003-1212-0117NICHT SPEZIFIZIERT
Pluta, AdamAdam.Pluta (at) dlr.dehttps://orcid.org/0000-0002-3423-3246NICHT SPEZIFIZIERT
Zech, MatthiasMatthias.Zech (at) dlr.dehttps://orcid.org/0000-0003-4420-5238NICHT SPEZIFIZIERT
Medjroubi, WidedWided.Medjroubi (at) dlr.dehttps://orcid.org/0000-0002-2274-4209NICHT SPEZIFIZIERT
Datum:8 September 2021
Erschienen in:Energies
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
DOI:10.3390/en14185642
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:1996-1073
Status:veröffentlicht
Stichwörter:pipeline detection, CNN, Landsat 5, U-Net, gas transport network
HGF - Forschungsbereich:Energie
HGF - Programm:Energiesystemdesign
HGF - Programmthema:Digitalisierung und Systemtechnologie
DLR - Schwerpunkt:Energie
DLR - Forschungsgebiet:E SY - Energiesystemtechnologie und -analyse
DLR - Teilgebiet (Projekt, Vorhaben):E - Energiesystemtechnologie
Standort: Oldenburg
Institute & Einrichtungen:Institut für Vernetzte Energiesysteme > Energiesystemanalyse, OL
Hinterlegt von: Dasenbrock, Jan
Hinterlegt am:05 Okt 2021 15:51
Letzte Änderung:28 Jan 2022 11:48

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.