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Article

An Oil Well Dataset Derived from Satellite-Based Remote Sensing

1
School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China
2
School of Computing, Ulster University, Belfast BT37 0QB, UK
3
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100101, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(6), 1132; https://doi.org/10.3390/rs13061132
Received: 26 February 2021 / Revised: 10 March 2021 / Accepted: 13 March 2021 / Published: 16 March 2021

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.

Graphical Abstract

1. Introduction

1.1. Background

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. Therefore, 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. Traditional methods for oil well detection includes mainly relying on an on-site survey. Collecting geo-information by conducting surveys of oil wells is impractical and time consuming especially for large scale monitoring. Optical remote sensing techniques can periodically monitor oil wells by obtaining both spatial and temporal information, which show advantages of unrestricted national boundaries and objectivity of observation.
Oil is generally extracted from underground reservoirs based on drilling and pumping approaches. As underground infrastructure of oil wells are not seen, we use the aboveground infrastructure of oil well pump jacks (Figure 1) to detect the oil wells. Pump jacks are small targets in remote sensing, they are generally 6–12 m long, 2–5 m wide, and 5–12 m high, which may slightly vary depending on the model of the pump jack. In a 0.5 m resolution satellite image, a pump jack only has dozens of pixels with a small amount of target information. As can be seen in Figure 1, the land background of oil well pump jacks varies significantly including wetlands, forests, deserts, etc. Moreover, due to the distinctions in the pump jack installation, even the pump jacks of the same model show completely different shape in the images. Furthermore, the surrounding trees and other types of machinery will increase the difficulty in the detection of oil wells.
In recent years, the development of remote sensing technologies and the rapid improvement in deep learning-based object detection methods make it possible to monitor oil wells from optical remote sensing images. Remote sensing techniques have the advantages of short-term, non-contact, and wide area coverage, repetitive monitoring in earth observation, and are also able to monitor the objects objectively and periodically [2]. Object detection from optical remote sensing images determines objects of interests including vehicles, buildings, airplanes, ships, etc. It also plays a major role in a wide range of applications in environment monitoring and urban planning [3]. The aboveground infrastructure of oil wells shown in Figure 2 have sharp boundaries, which make oil wells independent from background environments; therefore, it is possible to detect oil wells from optical remote sensing images. In addition, the well site and the surrounding facilities of the oil wells, and the road connecting to the well site enable the oil well detection from multiple scales.
Satellite-based remote sensing images are taken from a high-altitude angle of view. There are several challenges in oil well detection. The oil wells are small targets in the remote sensing images (see Figure 3). They are often in the complex backgrounds and different in observation angles, which lead to the uncertainties in remote sensing monitoring and identification. Those challenges in oil well detection from remote sensing images are shown in Figure 3.

1.2. Related Work

1.2.1. Oil-Related Monitoring Using Remote Sensing Techniques

Most of the current relevant studies in the areas of oil-related monitoring are focused on oil spill detection, offshore drilling platforms, and oil tank detection. Oil spill monitoring has been an important topic in the past few decades as it is related to environmental monitoring or disaster monitoring [4,5,6]. For oil spill area detection, a variety of techniques have been used including visible light [7], infrared [8], near-infrared [9], ultraviolet [4], hyperspectral [10], image analysis [11], and satellite radar [12]. In recent years, there has been a significant increase in the research for monitoring of offshore oil resources. Liu et al. [13] used the contextual features extracted from the Defense Meteorological Program/Operational Line-Scan System (DMSP/OLS) night light data. Good recognition results had been achieved on extracting the spatial position information of the South China Sea oil and gas drilling platform by using the time series Landsat-8 Operational Land Imager (OLI) image and layered screening strategy. In recent years, most of the research interests are among the area of remote monitoring of the existing oil tank detection [14] from satellite images. In [15], a traditional machine learning algorithm with the Speeded up Robust Features (SURF) technique and Support Vector Machine (SVM) classifier has been used for oil tank detection. More recently, deep learning-based solutions have been applied in the detection of oil tanks to address the high computational complexity of remote sensing images [16]. In the detection of circular oil storage tanks, a circular support model was established based on the radiological symmetry characteristics of the object, which was verified by GeoEye-1 high-score images [17]. Zhang et al. [18] proposed a recognition algorithm based on deep environment features by using the convolutional neural network (CNN) model and SVM classifier to recognize oil tanks from complex backgrounds and achieved high recognition performance.
Some researchers [19] have used high-resolution satellite images and the Faster regional convolutional neural network (R-CNN) deep learning network (Zhang, Liu et al., 2018) to identify oil facilities and achieved good results. The facilities in well site represents the distribution of oil wells in a certain sense, but the well site can be water injection wells or multiple oil well deployments, which does not represent the true distribution of oil wells. To the best of our knowledge, currently there is no research about the oil well identification based on satellite remote sensing images.

1.2.2. Smart Oilfield

Digital oilfield [20] platform automates the workflow and monitoring the operations in order to reduce the cost and risks. Smart oilfield [21] is a new trend in the oilfield development based on the improvement in the previous digital oilfield. In the last few decades, with the ever-increasing big data, internet of things (IoT), and artificial intelligence (AI), the development of smart oil field has been further enhanced. Different sensors including pressure, density, and temperature sensor have been proposed and deployed to improve the safe and efficient oil pump extraction [22]. Some studies have focused on the exploration and identification of oil wells based on dynamometer card. For example, the electrical parameters from dynamometer card was used for recognition of the working condition of oil wells [23]. Sun et al., used CNN to construct the recognition model of oil well function diagram [24]. The recent remote sensing technologies can be used to remotely monitor the oil wells/oil equipment periodically, which can potentially provide additional data in the area of the smart oilfield.

1.2.3. Deep Learning in Remote Sensing

Recently, with the advancement in the area of optical remote sensing and deep learning algorithms, researchers have been working on detection of various ground targets including the detection of buildings [25,26], ships [27,28,29], airplanes [30,31]. Monitoring oil wells from remote sensing images becomes possible with the advancement of the object detection algorithms based on deep learning frameworks. The hand crafted features were used in traditional machine learning based object detection methods, which are difficult to be applied in massive data scenarios. More recently, a deep learning-based method has replaced the traditional machine learning method by utilizing (more) higher level and deeper features. Training of the deep learning models requires large scales of the dataset, and there are different datasets available for ships, flights and buildings [32,33,34]. In terms of applications related to oil, the detection of oil spills [35,36,37] and oil storage tanks [16] from remote sensing images based on deep learning frameworks received significant attention in recent years. In terms of oil well monitoring, very limited research has been done. Our previous work [38] detected the oil wells from remote sensing images using a Faster regional convolutional neural network (R-CNN) based model, but only a two-stage method has been used.
The use of deep learning based detection methods in remote sensing requires large scale dataset of labeled optical remote sensing images. For example, ImageNet [39], Common Objects in Context (COCO) [40], and pattern analysis, statistical modelling and computational learning (PASCAL) Visual Object Classes (VOC) [41] are widely used publicly available datasets, but none of these datasets provides images for oil wells. To the best of our knowledge, there are no available open oil well satellite image datasets. Therefore, in this work, we construct an oil well dataset from Google Earth Images, which aims to explore the feasibility of automatically detecting and dynamic monitoring of oil wells from remote sensing images. The dataset contains 1192 oil wells in 432 images, which vary in orientation and background. Moreover, we apply different state-of-the-art deep learning frameworks on our dataset to explore the best performance model. Our dataset will be publicly available online [42].
The remaining part of this paper is organized as follows. Section 2 introduces the construction of the dataset. Section 3 describes the state-of-the-art deep learning frameworks, which are used in this work. Section 4 describes the experimental results of applying the state-of-the-art models on our dataset and presents the accuracy of different models. Results are discussed in Section 5, and the conclusion is presented in Section 6.

2. Oil Well Dataset

2.1. Images Collection and Pre-Processing

We constructed a dataset named Northeast Petroleum University–Oil Well Object Detection V1.0 (NEPU-OWOD V1.0) of oil wells that contains the geographical coordinates and labels for each of the oil wells. Our selected research region for this work is in Daqing, China. Daqing is located in North China and is known as “Oil Capital of China”. Our dataset covers an area of 369 square kilometers as seen in Figure 4. The sample images in our dataset are 768 × 768 pixels, 768 × 1024 pixels and 1024 × 1024 pixels. Our dataset contains 432 images which contain 1192 oil wells from Google Earth Images with a very high resolution of 0.41 m per pixel. Figure 5 shows the distribution of the number of oil wells per image. It is noted that in most of the images, there are only 1 or 2 oil wells. In order to increase the diversity of the dataset, we included the images with the different backgrounds and various orientations of the oil wells (see Figure 6).

2.2. Images Annotation/Labeling

The images annotation has been done by local experts and researchers using an open-source annotation tool—RSLabel [43]. Each labeled oil well is saved to an XML file, which includes the geo-location coordinates of the oil wells, images sizes, and bonding boxes sizes, as shown in Figure 7.

3. Methods

State-of-the-art methods/algorithms: in order to evaluate our dataset, we chosen several state-of-the-art deep learning-based frameworks/models for comparison (see Table 1). In general, the object detection algorithms are divided into two categories, including two-stage method and one-stage method.

3.1. Two-Stage Method

In the area of object detection based on deep learning neural networks, the convolutional neural network (CNN) lays the foundation. The first important approach in the two-stage method is regional convolutional neural network (R-CNN) [44], which initially extracts region proposal and computes features for each proposal based on CNN and then classifies each region. A number of improved approaches have been proposed based on R-CNN which include Fast R-CNN [45] and Faster R-CNN [46]. In this work, we mainly use Faster R-CNN based frameworks which firstly generate several regions of interests by using a Region Proposal Network and then send the proposed regions for object classification and bounding-box regression [47]. It is believed that the two-stage method can achieve higher accuracy rates but are typically slower compared with the one-stage method [48].

3.1.1. Faster R-CNN

In recent years, region proposal methods have been used in most of the object detection applications from the remote sensing images. Based on the first region-based convolutional neural networks (R-CNN) [44], new methods, including Fast R-CNN [45], and Faster R-CNN [46] have been proposed to improve the effectiveness and computation efficiency. As Faster R-CNN is better and faster, we only select Faster R-CNN for the second stage model and it has been used with different backbones (see Table 1). In this work, we used six different backbones for Faster R-CNN. Region Proposal Network (RPN) was proposed by Ren et al. [46] and it has been used in enormous application areas in object detection. For the objects from an input image, RPN is used to generate region proposals and an objectness score for each of the generated proposals. RPN consists of a classifier, which is used to determine the region proposal probability, and a regressor, which is used to regresses the proposal coordinates.
As shown in Figure 8, the deep convolutional networks extract the feature map from the input image, and Faster R-CNN uses convolutional network RPN to generate the region proposals. Then a Region of Interest (ROI) pooling layer is used to converting all the proposals to a fixed shape which is required by the final fully connected layer. ROI takes two inputs, from RPN and feature map, respectively, and flattens and passes them to two fully connected layers. Finally, two fully connected layers are applied to generate a prediction of the target and the best bounding box proposal.

3.1.2. Backbone Network

Backbone network works as a very important component in CNN based object detection frameworks [49]. In our evaluation, different existing backbone networks e.g., Residual Networks (ResNet) [50], Feature pyramid network (FPN) [51] with ResNet-50 and ResNet-101 have been utilized for feature extraction. In this work, for Faster R-CNN model we selected different combination of backbones including ResNet-50-FPN, ResNet-50-C4, ResNet-50-DC5, ResNet101-FPN, ResNet-101-C4, and ResNet-101-DC5. The evaluation of the performance of Faster R-CNN model with different backbone of oil well detections are presented in Section 4.

3.2. One-Stage Method

One-stage method (or single-stage method) is proposed in recent years to address the challenges in real-time object detection and it predicts straight from image pixels to bounding boxes and class probabilities utilizing only a single deep neural network. The most commonly used one-stage method includes You Only Look Once (YOLO) [52], Single Shot MultiBox Detector (SSD) [53], and RetinaNet [54]. These models usually run much faster compared with the two-stage method.

3.2.1. YOLO

YOLO is the first one-stage method based on convolutional neural network proposed by Redmon et al. [52]. It utilizes a unified architecture and predicts the bounding boxes location and target classification at the same time as a single regression problem by directly extracting the features from input images. Compared with the two-stage method, YOLO is faster. Different versions of YOLO have been proposed, e.g., YOLOv2 and YOLOv3, to improve the detection accuracy and shorten the running time. YOLOv2 [55] is the second version of YOLO and improves YOLO detection performance by incorporating a verity of techniques including batch normalization, high resolution classifier, convolutional with anchor boxes, dimension clusters, direct location predication, fine-grained features, and multi-scale training. YOLOv3 [56] is the third version of YOLO and has made further improvements on YOLO by utilizing a multi-label approach, which better models the data for complex dataset with overlapping labels. Moreover, three different scales were used in YOLOv3 for predicting bounding boxes based on the extracted feature maps and finally the last convolutional layers output three-dimensional (3D) tensor encoding bounding box, objectness, and class prediction. In this work, we used the YOLOv3 framework and the Darknet-53 as the backbone because Darknet-53 is better in detection accuracy and faster than ResNet, according to Redmon and Farhadi’s work [56]. The output predictions have been made at different scales. As in Figure 9, the YOLOv3 employs Darknet-53 as the backbone and then uses Darknet_conv2D_BN_Leaky (DBL) structures. As the basic element of YOLOv3, DBL structure is short for Darknet_conv2D_BN_Leaky which consists of a convolution layer, a batch normalization layer and a leaky ReLU layer [57]. Three different scale feature maps are used in the prediction stage.

3.2.2. SSD

SSD [53] is another one stage method that has been proposed in 2016 and it is a single-shot detector for multiple categories. It combines a feature extract and detection step based on a feed-forward convolutional network with a non-maximum suppression (NMS). Comparing with the two stage method, SSD removes the generation of the proposal region and incorporates the computation of features in just one stage. Compared with YOLO, SSD model adds several feature layers to the end of a base network [53]. As shown in Figure 10 below, SSD uses VGG16 to extract the feature map through using a Conv4_3 layer and a Pool5 layer. Eight more feature layers have been added after the base network (VGG16) in order to multi-scale feature maps for the detection. The use of multi-scale feature map significantly improves the detection accuracy.

3.2.3. RetinaNet

RetinaNet [54] is believed one of the best one-stage methods. It utilizes focal loss for the loss function, which addresses the class imbalance challenges among the one-stage method. The method chosen in this work uses ResNet-50-FPN backbone network as shown in Figure 11. In this framework, ResNet-50 is used to extract deep features. FPN is employed on top of the ResNet backbone to construct rich multi-scale feature pyramid from the input image. The probability of object presence at each spatial position and regresses the offset for bounding boxes are predicted by a classification and box regression subnet which is comprised of a fully convolutional network (FCN) attached to FPN.

4. Experimental Results

4.1. Training Details

In this work, we used our constructed dataset NEPU–OWOD V1.0, which contains 1192 oil wells from Google Earth Images with a very high resolution of 0.41 m per pixel. The size of images in our dataset varies from 768 × 768 pixels, 768 × 1024 pixels and 1024 × 1024 pixels. The training dataset included 345 images with 968 oil wells, which are randomly selected and the remaining is test dataset which included 87 images with 224 oil wells. All of the experiments were carried out on a server with Intel i9-9900KF CPU (3.60 GHz) and a NVIDIA GeForce RTX 2080Ti GPU (11264M).

Training Loss

The training loss changes are present in Figure 12 and Figure 13 for both two-stage methods and one-stage method, respectively. As shown in Figure 12, Faster R-CNN ResNet-101-DC5 (Figure 12d) is more robust with a faster convergence compared with other two-stage frameworks with different backbones used in this work. For one-stage model training loss (Figure 13), RetinaNet converges more quickly and has more robust training loss compared with YOLOv3 and SSD.

4.2. Evaluation Metrics

In this work, we mainly used several metrics, including precision, recall, F1 score, average precision (AP), and receiver operating characteristic (ROC) curve to evaluate different models proposed in Section 3.

4.2.1. Intersection over Union (IoU)

IoU, also known as Jaccard index, is one of the most commonly used evaluation metrics of object detection. IoU is computed as the Equation (1) below, it compares the similarities between the prediction and ground truth area.
I o U   =   A B A B = I U ,
where A and B are the prediction and ground truth bounding boxes, respectively. I is intersection Area and U is Union Area.

4.2.2. Precision, Recall, and F1 Score

Typically, we need to set a threshold of IoU to obtain the true positive (TP). When IoU > 0.5, the result is considered as TP.
P r e c i s i o n   =   T P T P + F P   ,
R e c a l l   =   T P T P + F N   ,
F 1 =   2 1 Precision + 1 Recall = 2 * Precision * Recall Precision + Recall ,
where TP is true positive, FP is false positive, and FN is false negative.

4.2.3. AP

AP combines recall and precision by computing the average precision over the recall and is defined as the area under the precision–recall curve as in Equation (5) below [58]. In object detection, the performance is usually evaluated by computing AP under different IoU thresholds [49]. AP50 and AP75, referring to the IoU threshold at 50% and 75%, are used, respectively. A higher AP value indicates a better performance.
A P   =   0 1 p r d r   ,

4.2.4. McNemar’s Test

In order to evaluate if different state-of-art-models are significantly different from each other, McNemar’s test [59] is used. Equation (6) below shows the calculation of McNemar’s test statistic. The p-value threshold of 0.05 is used and the p-value lower than this threshold will indicate a significant difference between the two models.
χ 2   =   ( b c ) 2 b + c   ,
where b is total number of test instances that the first model gets correct, but the second model gets incorrect, and c is total number of test instances that the first model gets incorrect, but the second model gets correct.

4.2.5. ROC Curve

ROC curve displays the sensitivity (TPR) on y-axis and (1-specitivity) (FPR) on x-axis graphically for varying cut-off points of the test values [60]. The area under the curve (AUC) is an important and effective measure for the performance of a model. A good model has an AUC close to 1, while a poor model has an AUC close to 0. Figure 14 shows the ROC curves for all nine state-of-the-art models used in this study. RetinaNet has the highest AUC (0.86) among all nine state-of-the-art models.

4.3. Experimental Results for State-of-Art Algorithms

4.3.1. Comparisons of Oil Well Detection Accuracy of Different State-of-the-Art Models

We compared nine different deep learning models for oil well detection. The comparison results are summarized in Table 2 and Table 3. The precision, recall and F1 score are presented for all of the deep learning models at IoU = 0.5 in Table 2. The results show that SSD model achieves the best precision with the shortest training time and lowest memory cost. Faster R-CNN with R101-DC5 backbone has the best performance in recall and F1-score. Faster R-CNN and RetinaNet models have relative high recall and F1-score. In Table 3, different AP have been calculated based different deep learning models. RetinaNet model has the best AP and AP50 compared with all the other models and Faster R-CNN with R50-FPN backbone has the best AP75. Figure 15 shows the samples of the detection results.
Table 4 presents the results of McNemar’s Test in order to explore whether the performance differences in different models are significant. Most of the Faster R-CNN models and RetinaNet model are statistically similar while Faster R-CNN ResNet50-DC5, SSD, and YOLOv3 show significantly differences with other models in model performance.

4.3.2. Comparisons of Oil Well Detection Accuracy in Different Background and Orientations

In order to further analyze the oil well detection performance under different background and at different orientations, we calculated the precision, recall, and F1-score for each background including bare land, trees, buildings, lakes, and also two different orientations of the oil wells which are horizontal and non-horizontal orientations. Table 5 shows the total number of the oil wells in different backgrounds and orientations in the test dataset. Table 6 shows the performance metrics for each of the background and orientations. It can be seen that the oil well detection accuracy varies with different backgrounds and orientations. The lake background has the best detection performance but there are only 10 images in the test dataset. Bare land background outperforms trees and buildings, which may due to the fact that the trees and buildings are more complicated. In terms of the orientation of the oil wells, it can be seen that the horizontal oil wells are easier to be identified than the non-horizontal oil wells.

5. Discussion

It is noted that many factors have influences on oil well detection, including the complex backgrounds and differences in observation angles, etc. Therefore, in our dataset, the oil wells in different backgrounds and orientations are included. Oil well clusters will be included in the future to further supplement this dataset.
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-CNN with various backbones and three models are one-stage detection models based on YOLOv3, SSD, and RetinaNet. The results presented in Section 4 show the SSD achieves the best performance with the highest precision (0.807) and SSD has the lowest training time and lowest memory cost in training. Faster R-CNN R101-DC5 model achieves the best recall (0.928) and F1 score (0.838). To further improve the model performance, we will focus on adjusting the structure of the current nine models to make them achieve better performance for oil well detection.
In the experimental results section, we also compared the model performance of the oil wells in different background and different orientations. For all of the models, the oil wells in the horizontal angle outperform the oil wells in the non-horizontal angle. It is known that the oil wells have the largest shadow when in the horizontal angle, which might work as a more distinguishable feature in training of the model. For oil well detection in all different backgrounds, the two stage model generally has better performance compared with the one stage method. For one stage models, RetinaNet outperforms SSD and YOLOv3 model. It is also noted that the background of lakes and bare land background have better performance than that of the trees and buildings. It may be due to the background of the trees and buildings make the image more complex.

6. Conclusions

In this work, we created an oil well remote sensing image dataset, NEPU–OWOD V1.0, which consists of 1192 oil wells in 432 images collected from Google Earth in Daqing, China. Several state-of-the-art models, including both two-stage methods and one-stage methods, were applied on this dataset. Nine state-of-the-art models were trained and tested on our dataset including Faster R-CNN based frameworks with five different backbones, SSD, YOLOv3, 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. The comparison between different methods and the results show that the deep learning based model is able to detect the oil wells effectively from optical remote sensing images. Moreover, this dataset will provide the opportunity for researchers to develop new object detection algorithms and improve the oil well detection in remote sensing. Researchers will be able to design new oil well detection algorithms utilizing our published dataset.

Author Contributions

Conceptualization, Z.W. and L.B.; methodology, Z.W., L.B., and G.S.; software, G.S.; validation, G.S. and J.Z.; formal analysis, G.S.; investigation, L.B., Z.W. and G.S.; resources, L.B., Z.W., and G.S.; data curation, L.B., Z.W., and G.S.; writing—original draft preparation, Z.W., L.B., G.S., and J.Z.; writing—review and editing, L.B., Z.W., G.S., J.T., M.D.M., R.R.B., and L.C.; visualization, G.S.; supervision, Z.W. and L.B.; project administration, L.C.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.

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.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://drive.google.com/drive/folders/1bGOAcASCPGKKkyrBDLXK9rx_cekd7a2u?usp=sharing, accessed on 30 December 2020.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Sample images of oil well pump jacks onsite images.
Figure 1. Sample images of oil well pump jacks onsite images.
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Figure 2. Sample images of oil well pump jacks from optical remote sensing images (red rectangle boxes show the location of the oil well pump jacks).
Figure 2. Sample images of oil well pump jacks from optical remote sensing images (red rectangle boxes show the location of the oil well pump jacks).
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Figure 3. The challenges in the detection of oil wells from remote sensing images. (a) Oil wells angle differences in remote sensing images. (b) Oil wells background differences in remote sensing images.
Figure 3. The challenges in the detection of oil wells from remote sensing images. (a) Oil wells angle differences in remote sensing images. (b) Oil wells background differences in remote sensing images.
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Figure 4. Map shows the image coverage of Northeast Petroleum University–Oil Well Object Detection V1.0 (NEPU–OWOD) V1.0 dataset.
Figure 4. Map shows the image coverage of Northeast Petroleum University–Oil Well Object Detection V1.0 (NEPU–OWOD) V1.0 dataset.
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Figure 5. Distribution of the number of oil wells per image of NEPU–OWOD V1.0 dataset.
Figure 5. Distribution of the number of oil wells per image of NEPU–OWOD V1.0 dataset.
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Figure 6. Sample images from NEPU–OWOD V1.0 dataset.
Figure 6. Sample images from NEPU–OWOD V1.0 dataset.
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Figure 7. Oil wells annotation details.
Figure 7. Oil wells annotation details.
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Figure 8. Faster R-CNN framework.
Figure 8. Faster R-CNN framework.
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Figure 9. You Only Look Once (YOLO) v3 framework (with Darknet-53 backbone).
Figure 9. You Only Look Once (YOLO) v3 framework (with Darknet-53 backbone).
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Figure 10. SSD framework.
Figure 10. SSD framework.
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Figure 11. RetinaNet framework.
Figure 11. RetinaNet framework.
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Figure 12. Training loss for two-stage method models.
Figure 12. Training loss for two-stage method models.
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Figure 13. Training loss for one-stage method models.
Figure 13. Training loss for one-stage method models.
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Figure 14. Receiver operating characteristics (ROC) curve for all the models.
Figure 14. Receiver operating characteristics (ROC) curve for all the models.
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Figure 15. Object detection results of all the state-of-the-art models. The red boxes represent the detected oil wells.
Figure 15. Object detection results of all the state-of-the-art models. The red boxes represent the detected oil wells.
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Table 1. State-of-the-art deep learning models used in this work.
Table 1. State-of-the-art deep learning models used in this work.
ModelBackbone
Faster R-CNNResNet-50-FPN
ResNet-50-C4
ResNet-50-DC5
ResNet-101-FPN
ResNet-101-C4
ResNet-101-DC5
SSDVGG16
YOLOv3Darknet-53
RetinaNetResNet-50-FPN
Table 2. Oil well detection evaluation metrics (Precision, Recall, and F1 score) (Intersection over Union (IoU) = 0.5). The bold font indicates the best performance.
Table 2. Oil well detection evaluation metrics (Precision, Recall, and F1 score) (Intersection over Union (IoU) = 0.5). The bold font indicates the best performance.
ModelBackbonePrecisionRecallF1 ScoreTraining Time (Min)Memory Cost
(M)
Faster_R-CNNR50-FPN0.6840.9190.7841022131
R50-C40.7420.8750.8033194379
R50-DC50.7380.8970.8101715688
R101-FPN0.7240.8790.7941425200
R101-C40.7340.9240.8183545211
R101-DC50.7640.9280.8381967130
SSDDarknet-530.8070.6740.734331803
YOLOv3VGG160.1660.2670.205925107
RetinaNetR50-FPN0.6450.8920.7491476768
Table 3. Oil well detection average precision (AP) of nine state-of-art algorithms. The bold font indicates the best performance.
Table 3. Oil well detection average precision (AP) of nine state-of-art algorithms. The bold font indicates the best performance.
ModelBackboneAPAP50AP75Training Time (min)Memory Cost
(M)
Faster_R-CNNR50-FPN52.94189.54756.9741022131
R50-C448.76885.78551.9823194379
R50-DC550.23089.46948.9691715688
R101-FPN49.17784.86349.2021425200
R101-C452.29288.72056.0133545211
R101-DC550.03487.40852.9351967130
SSDDarknet-5343.29885.85336.103331803
YOLOv3VGG163.6987.6533.061925107
RetinaNetR50-FPN55.52999.79555.3171476768
Table 4. p-value of McNemar’s Test for comparing different deep learning model performances. p-values under 0.05 indicate the two compared models are significantly different. (The bold font indicates models that were statistically different).
Table 4. p-value of McNemar’s Test for comparing different deep learning model performances. p-values under 0.05 indicate the two compared models are significantly different. (The bold font indicates models that were statistically different).
Faster_R-CNNSSDYOLO
v3
RetinaNet
McNemar’s Chi Squared (p-Value)R50-
FPN
R50-C4R50-
DC5
R101-
FPN
R101-
C4
R101-
DC5
Darknet-53VGG16R50-
FPN
Faster_R-CNNR50-FPN
R50-C40.025
R50-DC510.008
R101-FPN0.1800.2850.083
R101-C410.01810.083
R101-DC510.01810.1091
SSDDarknet-53<0.0010.002<0.001<0.001<0.001<0.001
YOLOv3VGG16<0.001<0.001<0.001<0.001<0.001<0.001<0.001
RetinaNetR50-FPN0.5930.0740.6370.3710.6370.637<0.001<0.001
Table 5. Total number of oil wells in the test dataset of different background and orientation.
Table 5. Total number of oil wells in the test dataset of different background and orientation.
Bare LandTreesBuildingsLakesHorizontalNon-
Horizontal
Total number of the oil wells in the test dataset14836301015767
Table 6. Oil well detection performance in different backgrounds and orientations (precision, recall, and F1 score (IoU = 0.5)) (The bold font indicates the best performance for each performance metric).
Table 6. Oil well detection performance in different backgrounds and orientations (precision, recall, and F1 score (IoU = 0.5)) (The bold font indicates the best performance for each performance metric).
ModelBackbonePerformance MetricBare landTreesBuildingsLakesHorizontalNon-
Horizontal
Faster_R-CNNR50-FPNPrecision0.6230.4590.4720.9090.5690.401
Recall0.9260.9440.8331.0000.9240.821
F1 score0.7450.6180.6020.9520.7040.539
R50-C4Precision0.6900.5000.4131.0000.6210.514
Recall0.9190.8610.6331.0000.8850.851
F1 score0.7880.6330.5001.0000.7300.640
R50-DC5Precision0.6880.4430.4770.9090.6210.504
Recall0.9390.8610.7001.0000.8980.896
F1 score0.7940.5850.5680.9520.7340.645
R101-FPNPrecision0.6590.4840.5000.9090.6040.492
Recall0.9120.8330.7331.0000.8850.866
F1 score0.7650.6120.5950.9520.7180.627
R101-C4Precision0.6640.5000.4811.0000.6060.508
Recall0.9460.8890.8331.0000.9300.910
F1 score0.7800.6400.6101.0000.7340.652
R101-DC5Precision0.6880.4430.4770.9090.6460.541
Recall0.9390.8610.7001.0000.9430.896
F1 score0.7940.5850.5680.9520.7670.674
SSDDarknet-53Precision0.7350.4520.5361.0000.7030.530
Recall0.7300.5280.5000.9000.7390.522
F1 score0.7320.4870.5170.9470.7200.526
YOLOv3VGG16Precision0.1720.0740.1230.0000.0930.195
Recall0.3110.1940.2330.0000.1780.478
F1 score0.2210.1070.1610.0000.1230.277
RetinaNetR50-FPNPrecision0.6040.3940.4240.9090.5690.401
Recall0.9260.7780.8331.0000.9240.821
F1 score0.7310.5230.5620.9520.7040.539
Precision0.6140.4170.4340.8380.5590.454
AveragesRecall0.8390.7500.6660.8780.8120.785
F1 score0.7060.5320.5200.8560.6590.569
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Wang, Z.; Bai, L.; Song, G.; Zhang, J.; Tao, J.; Mulvenna, M.D.; Bond, R.R.; Chen, L. An Oil Well Dataset Derived from Satellite-Based Remote Sensing. Remote Sens. 2021, 13, 1132. https://doi.org/10.3390/rs13061132

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Wang Z, Bai L, Song G, Zhang J, Tao J, Mulvenna MD, Bond RR, Chen L. An Oil Well Dataset Derived from Satellite-Based Remote Sensing. Remote Sensing. 2021; 13(6):1132. https://doi.org/10.3390/rs13061132

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Wang, Zhibao, Lu Bai, Guangfu Song, Jie Zhang, Jinhua Tao, Maurice D. Mulvenna, Raymond R. Bond, and Liangfu Chen. 2021. "An Oil Well Dataset Derived from Satellite-Based Remote Sensing" Remote Sensing 13, no. 6: 1132. https://doi.org/10.3390/rs13061132

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