An Oil Well Dataset Derived from Satellite-Based Remote Sensing

Zhibao Wang; Lu Bai; Guangfu Song; Jie  Zhang; Jinhua Tao; Maurice Mulvenna; RR Bond; Liangfu Chen

doi:10.3390/rs13061132

An Oil Well Dataset Derived from Satellite-Based Remote Sensing

Zhibao Wang
, Lu Bai
, Guangfu Song
, Jie Zhang
, Jinhua Tao
, Maurice Mulvenna
, RR Bond
, Liangfu Chen

Southwest Petroleum University China

Research output: Contribution to journal › Article › peer-review

32 Citations (Scopus)

167 Downloads (Pure)

Abstract

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

Original language	English
Article number	1132
Pages (from-to)	1-21
Number of pages	21
Journal	Remote Sensing
Volume	13
Issue number	6
DOIs	https://doi.org/10.3390/rs13061132
Publication status	Published (in print/issue) - 16 Mar 2021

Bibliographical note

Funding Information:
Funding: This work was supported in part by TUOHAI special project 2020 from Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002 and project of Excellent and Middle‐aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

Funding Information: Funding: This work was supported in part by TUOHAI special project 2020 from Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002 and project of Excellent and Middle‐aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Keywords

Deep learning
Oil well dataset
Oil well detection
Optical remote sensing
Satellite imagery

Access to Document

10.3390/rs13061132Licence: CC BY

remotesensing-13-01132Final published version, 4.57 MBLicence: CC BY

Cite this

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title = "An Oil Well Dataset Derived from Satellite-Based Remote Sensing",

abstract = "Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.",

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N2 - Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

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An Oil Well Dataset Derived from Satellite-Based Remote Sensing

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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