Semantic Segmentation of Oil Well Sites Using Sentinel-2 Imagery

The number and geographical location of oil well sites can reflect the local oil production situation and there is a growing interest in automatically identifying oil well sites from remote sensing images. Traditionally, visual interpretation was employed to extract oil well sites locations from rem...

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Bibliographic Details
Main Authors: Wu, Hao, Dong, Hongli, Wang, Zhibao, Bai, Lu, Huo, Fengcai, Tao, Jinhua, Chen, Liangfu
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
oil well sites
Oils
Production
Satellite images
Satellites
Semantic segmentation
Sentinel 2
Visualization
Online Access:Request full text
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Summary:The number and geographical location of oil well sites can reflect the local oil production situation and there is a growing interest in automatically identifying oil well sites from remote sensing images. Traditionally, visual interpretation was employed to extract oil well sites locations from remotely sensing images. However, this approach is time-consuming and heavily dependent on domain experts. Advancements in remote sensing satellite technology and the widespread use of deep learning algorithms have enabled the automated extraction of oil well sites from remote sensing images. In this paper, we established the Northeast Petroleum University Oil Well Sites Dataset Version 1.0 (NEPU-OWS V1.0), and to evaluate its usability by comparing several different deep learning models based on semantic segmentation algorithms for optical remote sensing images. Experimental results show that current advanced deep learning models achieve high accuracy on this dataset, demonstrating great potential for remote sensing detection in oil well sites.
ISSN:2153-7003
DOI:10.1109/IGARSS52108.2023.10282739