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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.
Highlights
In order to increase the diversity of the dataset, we included the images with the different backgrounds and various orientations of the oil wells
We trained and tested our dataset on nine different state-of-the-art models, in which six models are two-stage detection models based on Faster R-convolutional neural network (CNN) with various backbones and three models are one-stage detection models based on YOLOv3, SSD, and RetinaNet
The experimental results show that deep learning-based methods are effective in the detection of the oil wells from the remote sensing images
Summary
Oil remains one of the largest energy sources globally despite the fact that renewable energy will be the fastest growing energy, according to BP’s 2019 Energy Outlook [1]. Petroleum production can affect soil and water (e.g., oil spills), which presents potential risks to the environment and public health. The monitoring of the number and geological distribution of oil wells, the status of oil exploitation, and an established energy early warning mechanism are essential, especially to the policy makers in energy resource management. It is crucial to build an automatic oil well detection capability in order to address the challenges in oil well resources planning and environment monitoring
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