Detection of over-ground petroleum and gas pipelines from optical remote sensing images

• Published in: Image and Signal Processing for Remote Sensing XXIX
• Main Authors: Chang, Huan, Bai, Lu, Wang, Zhibao, Wang, Mei, Zhang, Ying, Tao, Jinhua, Chen, Liangfu
• Other Authors: Bruzzone, Lorenzo, Bovolo, Francesca
• Format: Article in Journal/Newspaper
• Language: English
• Published: SPIE - The International Society for Optical Engineering 2023
• Subjects: /dk/atira/pure/sustainabledevelopmentgoals/decent_work_and_economic_growth; SDG 8 - Decent Work and Economic Growth; permafrost
• Online Access: https://pure.qub.ac.uk/en/publications/f55ed0b8-2e36-4933-9420-0e9275f0f337; https://doi.org/10.1117/12.2683053; https://pureadmin.qub.ac.uk/ws/files/539613784/SPIE_ch_final.pdf

Petroleum and gas pipelines, comprising petroleum and gas pipes and related components, play an irreplaceable role in petroleum and gas transportation. For global economic growth, petroleum and gas are crucial natural resources. However, the pipelines often cross permafrost regions with challenging working conditions. Additionally, the potential for natural disasters raises concerns about pipeline accidents, posing a threat to pipeline operational safety. In response to the complexity of pipeline supervision and management, we choose to use remote sensing method combining deep learning-based algorithms. In this work, we build a petroleum and gas pipes dataset, which includes 1,388 remote sensing images and the study area is Russian polar regions. We trained FCN and U-Net deep learning models by using our self-built dataset for the detection of petroleum and gas pipes. Models’ performances were evaluated using MIoU (Mean Intersection over Union), mean precision, mean recall to evaluate the accuracy of the model’s prediction results and compared them visually with ground truth. Our results find that deep learning models can effectively learn the characteristics of pipelines and achieve ideal detection results on our dataset. The MIoU of the FCN model achieved 0.885 and the U-Net model achieved 0.894. The results demonstrate that our trained models can be used to accurately identify the petroleum and gas pipelines in remote sensing images.