An oil well dataset derived from satellite‐based remote sensing

Zhibao Wang; Lu Bai; Guangfu Song; Jie Zhang; Jinhua Tao; Maurice D. Mulvenna; Raymond R. 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 D. Mulvenna, Raymond R. Bond, Liangfu Chen

^*Corresponding author for this work

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

25 Citations (Scopus)

33 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 auto-matically 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
Number of pages	21
Journal	Remote Sensing
Volume	13
Issue number	6
DOIs	https://doi.org/10.3390/rs13061132
Publication status	Published - 16 Mar 2021
Externally published	Yes

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.

Keywords

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

ASJC Scopus subject areas

General Earth and Planetary Sciences

Access to Document

10.3390/rs13061132Licence: CC BY

An oil well dataset derived from satellite‐based remote sensing
Copyright 2021 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 11.1 MBLicence: CC BY

Cite this

@article{212fa354740b42d8b9768d6fe2f02a64,

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 auto-matically 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.",

keywords = "Deep learning, Oil well dataset, Oil well detection, Optical remote sensing, Satellite imagery",

author = "Zhibao Wang and Lu Bai and Guangfu Song and Jie Zhang and Jinhua Tao and Mulvenna, {Maurice D.} and Bond, {Raymond R.} and Liangfu Chen",

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: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = mar,

day = "16",

doi = "10.3390/rs13061132",

language = "English",

volume = "13",

journal = "Remote Sensing",

issn = "2072-4292",

publisher = "MDPI",

number = "6",

}

TY - JOUR

T1 - An oil well dataset derived from satellite‐based remote sensing

AU - Wang, Zhibao

AU - Bai, Lu

AU - Song, Guangfu

AU - Zhang, Jie

AU - Tao, Jinhua

AU - Mulvenna, Maurice D.

AU - Bond, Raymond R.

AU - Chen, Liangfu

N1 - 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.

PY - 2021/3/16

Y1 - 2021/3/16

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 auto-matically 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.

AB - 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 auto-matically 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.

KW - Deep learning

KW - Oil well dataset

KW - Oil well detection

KW - Optical remote sensing

KW - Satellite imagery

U2 - 10.3390/rs13061132

DO - 10.3390/rs13061132

M3 - Article

AN - SCOPUS:85103245081

SN - 2072-4292

VL - 13

JO - Remote Sensing

JF - Remote Sensing

IS - 6

M1 - 1132

ER -

An oil well dataset derived from satellite‐based remote sensing

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this