Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Authors: Pratinav Seth
Michelle Lin
BREFO DWAMENA YAW
Jade Boutot
Mary Kang
David Rolnick
Venue: ICML 2025 poster (2025)
Keywords: Remote Sensing
Satellite Imagery
Climate Change
AI for Good
Primary area: applications->everything_else
TL;DR: We introduce a large benchmark dataset for pinpointing abandoned oil and gas wells, a significant contributor to climate change and pollution.

Abstract: Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the groundwater. Many of these locations are unknown, preventing the wells from being plugged and their polluting effects averted. Remote sensing is a relatively unexplored tool for pinpointing abandoned wells at scale. We introduce the first large-scale Benchmark dataset for this problem, leveraging high-resolution multi-spectral satellite imagery from Planet Labs. Our curated Dataset comprises over 213,000 wells (abandoned, suspended, and active) from Alberta, a region with especially high well density, sourced from the Alberta Energy Regulator and verified by domain experts. We evaluate baseline algorithms for well detection and segmentation, showing the promise of computer vision approaches but also significant room for improvement.

Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Pratinav Seth" 12 Michelle Lin 13
Brefo Dwamena Yaw
Jade Boutot
Mary Kang4 David Rolnick 143
Abstract
wells (Williams et al., 2020), releasing over five million metric tons of CO2 equivalent emissions per year. Despite the small percentage, the high global warming potential of methane underscores the need for improved monitoring and management to reduce environmental impact. Abandoned wells also pose health and safety concerns, in particular by leaching toxic chemicals into the groundwater of surround- ing communities (Cahill et al., 2019). It is possible to plug abandoned wells to mitigate the harms associated with them (with so-called "super-emitter" wells an especially high pri- ority (Riddick et al., 2024; Kang et al., 2016)). However, a significant fraction of abandoned wells remain unknown. In Pennsylvania, as much as 90% of abandoned wells are estimated to be unrecorded (Kang et al., 2016). In Canada, abandoned wells have been described as the most uncer- tain source of methane emissions nationally due to the poor quality of data surrounding them (Williams et al., 2020).
Millions of abandoned oil and gas wells are scat- tered across the world, leaching methane into the atmosphere and toxic compounds into the ground- water. Many of these locations are unknown, pre- venting the wells from being plugged and their polluting effects averted. Remote sensing a rela- tively unexplored tool for pinpointing abandoned wells at scale. We introduce the first large-scall Benchmark Dataset for this problem, leveraging high-resolution multi-spectral satellite imagery from Planet Labs. Our curated Dataset comprises over 213,000 wells (abandoned, suspended, and active) from Alberta, region with especially high well density, sourced from the Alberta Energy Regulator and verified by domain experts. We evaluate baseline algorithms for well detection and segmentation, showing the promise of com- puter vision approaches but also significant room for improvement.
With the advent of large-scale remote sensing datasets and powerful machine learning tools to process them, it has be- come possible to label and monitor the built environment as never before (Rolf et al., 2024). Many such works have focused on opportunities use remote sensing to accel- erate climate action and environmental protection, and oil and gas infrastructure has increasingly been an object of scrutiny (see e.g. Yang et al. (2013); Sheng et al. (2020)). In this work, we present the first large-scale machine-learning benchmark dataset for pinpointing onshore oil and gas wells, encompassing abandoned, suspended, and active wells. Our main contributions are as follows:
1. Introduction
Across the world, there are millions of abandoned oil and gas wells left to degrade by the companies or individuals that built them. No longer producing usable fossil fuels, these wells nonetheless have a significant impact on the en- vironment, with many of them leaking significant quantities of methane, a powerful greenhouse gas, into the atmosphere. In aggregate, these emissions represent the equivalent of millions of tons of carbon dioxide per year (Williams et al., 2020). In Canada, an estimated 370,000 abandoned wells produce methane equivalent to half a million metric tons of CO2 annually (Williams et al., 2020; ECCC, 2024), while in the U.S., there are an estimated 4 million abandoned
We introduce the Alberta Wells Dataset, containing information on over 200k abandoned, suspended, and active onshore oil and gas wells with high-resolution satellite imagery. Our Dataset available at: https:
/ / zenodo. wttp///orrd.///ooootoo../ooooeee 113743323.
We frame the problem of identification of wells as challenge for object detection and binary segmentation.
Equual contribution 'Mila Quchee AI Institute, Montreal, Canada 'Department of Data Science & Computer Application, Manipal Institute of Technology. Manipal Academy of Higher Education, Manipal, Karnataka, India UUniversité de Montréal, Montreal, Canada MMeiill University, Montreal, Canada. Corre- spondence to: Pratinav Seth <seth, seett.praainave @ @gmail.com>..........
We evaluate a wide range of state-of-ther the-tr--rttttttt deep learn- ing algorithms, showing promising performance but emphasizing the challenging nature of the task.
We hope that this work will represent a step towards scalable identification of abandoned well sites and the reduction of their deleterious effects on our climate and environment.
Proceedings of the nnnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 20255 by the author(s).

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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Table 1. Previous datasets in which remote sensing algorithms are applied detect oil and gas wells. "N/A" is given for datasets that do
not indicate the number of individual wells in the dataset.
Dataset
O&G Well
Total Well
Resolution (m/pxx
Geography
Imagery Source
Count
Images
NEFU-OWOD ( (Wang et al., 2021)
1,192
432
0.41
Doqing City. China
NEPU-OWOD V3.0 (Zhang et al., 2023)
3,749
722
0,48
China California
Oil Well Dataset (Shi et al., 2021)
NIA
5,895
0.26
Duging City. China
O&G Infrastructure (Guisiano et al., 2024)
630
930
0.15
Permian Basin, USA
Permiun and Denver
Google Earth
Well Pad Dataset (Ramachandran et al., 2024)
12,490
10,432
0.3-0.7
Basins, USA
NEPU-OWS (W et al., 2023bb
1,200
10
NEPU NOS.-OWSV.DD (Wu et al., 2023a)
N/A
120
10/20/60
Austin, USA
Sentinel 2
Alberta Well Datasel (Ours)
213,447
94,343
3
Alberta, Canada
Planet Labs
2. Previous Work
Table 2. Statistics of instances and wells across the dataset.
Hundreds of satellites continuously monitor the Earth's surface, generating petabyte-scale remote sensing datasets (Rolf et al., 2024). With advancements in hardware, the quality of remote sensing images has significantly im- proved in terms of spatial and temporal resolution. High- quality remote sensing data are available through state- funded projects like Sentinel and Landsat and, more recently, through private enterprises such Planet Labs (PBC, 2024). Increasingly, machine learning has been used parse such raw data, including in various applications for tackling cli- mate change (Yang et al., 2013). Benchmark datasets in this area have included tasks in land use and land cover (LULC) estimation (Sumbul et al., 2019), crop classification (Tseng et al., 2021), and forest monitoring (Bountos et al., 2025). Although developed primarily for urban monitoring, the SpaceNet 7 dataset (Van Etten et al., 2021) features small number of study sites with oil wells.
Wells/
Count of Well Type in
Split
Total Patches
Non-Wells Patchos
Wells Patches of Split Suspended
Abandoned
Active
Train Validation
167436 9463
83718
46342
47595
100294
4731
3166
2671
2406
Test
11789
5894
4024
3609
3340
their applicability in identifying abandoned or suspended wells as summarized in Table 1. Most of these studies have primarily focused on basic machine learning algorithms for well detection due to the limited sample size.
3. Alberta Wells Dataset
We introduce the Alberta Wells Dataset for oil and gas well detection. The dataset is drawn from the province of Al- berta, Canada, region with the third-largest oil reserves in the world and a substantial number of oil and gas wells, many of which have been present for over a century. The entire province of Alberta (an area larger than the UK and Germany combined) encompasses diverse range of geo- graphical zones and is highly diverse for a landlocked region, including prairies, lakes, forests, and mountains. The dataset contains over 188,000 patches of satellite imagery acquired by Planet Labs (PBC, 2024), which we make publicly avail- able, with around 94,000 containing wells covering more than 21 213,000 individual wells. (More Details in Table 2). Each patch is annotated with labels for both segmentation and bounding box localization. The annotations are based data from the Alberta Energy Regulator (AER, 2024), quality-controller by domain experts.
Within this area of research, an increasing body of work has considered the problem of detecting artifacts associated with oil and gas operations. The detection of oil spills using a combination of remote sensing and machine learning has been widely explored (Wang et al., 2023a; Yang et al., 2022). Recently, the detection of oil and gas infrastructure has also been investigated (Prajapati et al., 2022), with some studies focusing on the goal of estimating methane emissions (Zhu et al., 2022; Omara et al., 2023). The dataset by Sheng et al. (2020) includes 7.066 acrial images, with 149 images of oil refineries. The METER-ME dataset (Zhu et al., 2022) comprises 86,599 georeferenced images in the U.S. labeled for methane sources. The OGIM v1 dataset (Omara al., 2023) includes 2.6 million point locations of major facili- ties. A dataset by Chang et al. (2023) features 1,388 images of pipelines in the Aretic, while a dataset by Wang et al. (2023b) includes 3,266 images of heavy- enter- prises with 0.25 m resolution.
Our dataset attempts to maximize the amount of data avail- able for learning by including a mixture of active and sus- pended wells alongside abandoned wells. These types of wells appear overall similar in satellite imagery. In contrast to abandoned wells, "suspended" refers to wells that have merely paused operations temporarily, though this desig- nation can be inaccurate, and some wells are classified as suspended for long enough that they are truly abandoned. Active wells are those that are currently in operation.
The problem of detection of oil and gas wells has also been proposed by a number of authors. However, Existing datasets are quite small (500-10, 10,000) and typically are lim- ited to a small region and contain only active wells, limiting
To simulate real-world conditions, we ensure a varied den-
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
1822
(a) Training set
(b) Validation set
(c) Test set
Figure 1. Distribution of the number of individual wells in positive samples from the dataset. We also include an equal number of images with no wells at all.
Table 3. Information on the numbers of wells represented in the dataset across different states (suspended, abandoned, and active). It also includes domain-specific metadata, such the mode of operation and the types of fossil fuels extracted, which were used for filtering and quality control.
Well State
Count
License Status
Mode Shoet Description
Fluid Short Description
Suspension
All
Suspended
$5007
Issued Amended
Suspended
Gas, Crude oil, Crude bitumen.
Liquid petroleum gas.
Abundoned
All
Coalbed methune coals other Lith,
Ahandoned
54947
Issued
Ahundrned, Abandoned Zane,
Coalbed methane-cone coalls only.
Amended
Junked and Abandoned.
Shale gas only, Acid gas,
Isuad
Flowing, Pamping,Gas Gas Lift.
CBM and shale and other
Active 107139
Amended
Shale gas and other sources.
Re-Entered
Abandoned and Re-Entered
sity of wells per image, as highlighted in Figure 1. We also include satellite imagery patches with no wells present from areas nearby to areas with wells, ensuring no overlap between the samples. This balanced dataset maintains equal distribution of well and non-well images. Table 2 details the total sample count in each dataset split.
abandoned, or suspended based on criteria developed in consultation with domain experts, as shown in Table 3. We check for duplicate location coordinates in the dataset and resolve them by retaining the instance with the latest drill date. Finally, we ensure all the well instances in the dataset are within Alberta's 's boundaries. The raw metadata file has around 637,00 337,000 instances, while the surface hole geometry file has around 512,000 instances. After filtering and per- forming quality control on the datasets with domain experts, we have around 217,000 instances. We then calculate the ge- ographical bounds covered by the well instances across the province and divide the region into non-overlapping square image patches, each covering an area of 1.1025 sq km (with sides of 1050m). These images include various numbers of individual wells (see Figure 1), and we ensure that an approximately equal number of patches exist with and with- out wells. As a result of this process, some samples were excluded due to being located outside Alberta's geograph- ical boundaries, leading to final total of approximately 213,000 well instances in the dataset patches.
3.1. Data Collection, Quality Control & Patch Creation
The Alberta Energy Regulator (AER) oversees the energy industry in the province, ensuring companies adhere to regu- lations as they develop oil and gas resources. AER publishes AER ST37 (AER, 2024), a monthly list of all wells reported in Alberta, detailing their geographic location, mode of op- eration, license status, and type of product being extracted, among other attributes. This data provides a metadata (.txt) file and shhp shape file, where each entry represents unique geo-location point per site but often contains dupli- cates. However, this data cannot be used directly because the license status or mode of operation does not always cor- relate with the actual status of the well and often contains duplicates. Therefore, we work with domain experts to per- form quality control illustrated in Figure 5 (Appendix).
3.2. Dataset Splitting
To create well-distributed dataset that represents various geographical regions and offers a diverse dataset for eval- uation, we developed a splitting algorithm (A (Algorithm 1) which focuses on balancing regions, not individual exam- plcs, ensuring that both the training and test sets reflect a diverse range of regions from Alberta's varied landscape. This approach preserves dataset diversity and simulates real- world conditions where imbalances are common.
First, we remove duplicate entries from the well metadata, which often contain multiple instances of the same well identified by duplicate license numbers. We resolve these duplicates by retaining the most recent update. A similar approach is applied to the shapefile, where duplicates are resolved using the license date. Afterward, we merge both datasets and filter the data, categorizing the wells as active,
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
(a) Step-1 kis clusters
(b) C2s cluster centroids
(c) Step-2 kos clusters
(d) Final Dataset Split
Figure 2. Illustration of the outcome of applying our datasct splitting algorithm: In Figures (a) to (c), different colors represent various cluster IDs. In Figure (d), blue refers to the training set, orange to the validation set, and green to the test set.
Algorithm 1 Clustering Algorithm for Dataset Splitting
This method involves forming small clusters ky of nearby well patches based on their centroids as illustrated in Figure (a). These small clusters are then grouped into larger, non-intersecting super-clusters k2s, with each super-cluster representing a city or larger geographical area. The for- mation of super-clusters involves calculating a centroid for each kns cluster based on the centroids of the well patches it contains as illustrated in Figure 2 (b). By clustering wells in this manner, we ensure that kis clusters group wells from nearby localities together, while k2; clusters group wells from the same geographic region as illustrated in Figure 2 (c). Thus, each k2; cluster represents geographic distribu- tion, with each kns cluster within it representing sample of that distribution. To ensure a diverse and well-distributed evaluation and testing of our machine learning model, we select the ki; clusters with the two fewest well instances from each l2 super-cluster for inclusion in the evaluation and test sets. This approach ensures diverse representation of the dataset as observed in Figure 2 (d). Moreover, we maintain an equal distribution of well and non-well patches. In cases of imbalance in non-well images, we exclude such patches from the contributing kis clusters as specified in Algorithm 1. For imbalances in well images, we sample non-well patches that are not part of any other clusters.
W: Set of image patches ids containing wells; ; NW: Set of image patches ids not containing wells
Input: represents the i-th patch with centroid coordinates ou where W or NW:
Output: T.: Test :e: Train Set: E.; Eval Set:
Step I: Clustering into Clusters
Perform K-Means Clustering ly (+) with M clusters using controid coordinates
e, where E W.
Assign each i-th patch into the mith cluster where m E =(....M) and i e W:;
cluster ki. ke())))
and update patches (zz,, et, k1z)
for = E {11,.. {1......d. MM) do
Wes [je W || = =}
Calculate cluster centroids 02g hased on values of o and update patch:
(at, C,, k1. (22). where Wes.
end for
Step Clustering into Super Clusters
Let Wee be the set of unique egj for W
Perform K-Means clustering (*) with N clusters using all cas E Wee- Assign each eg; Wee n-th cluster, where E(I...N) & Rg (e2:) = n.
Update patches (zy.e,, C1y. kay) where czy ca. and j W. Step 3: Assigning Patches to Sets for a E {1, .h....... W.Jdd do
where j E W as Wys- Find unique k and count oy associated with it for j in Wy. The, assign kij
Find all j with k2s
with minimum counts ming and ming.
For each é in Wa., appond i to E. if R1. min,, to T, if Ris = ming. otherwise Ty. end for
Step Assigning Kon-Well Patches
for each sel_connter [E. T.. do
for each unique kiy as 2; E sont.counter de
Find convex bull radius r(2;) of accupied by oy where j
sel.counter & Kig = Zi-
Locate non-well patches é NW within radius r(a.) in any other cluster, Assign f to cluster 2; (z Cr, kiy): ky = #1
The parameters used in constructing the dataset are M 300 and N = 30. These were picked heuristically so as to create a well-distributed dataset. Alberta's 's varied landscape offers a rich environment for creating a comprehensive oil well dataset. Training machine learning models on this ex- tensive dataset improves their robustness and ability to gen- eralize to similar, less-studied regions, thereby supporting well detection and efforts mitigate global warming. By forming non-overlapping clusters (A1,), each with its own well and non-well patches, we minimize the risk of data leakage while ensuring diversity. We also balanced non- well images across clusters to better simulate real-world conditions and maintain the diversity of the dataset.
end for
Step 5: Imbalance Correction
Tw refers Count of Well Instances& T, refers Count Non-Well Instances
in Dataset Split
if Thu & TT, then
Identify clusters kng in data split contributing to the imhalance of excess non- well patches, assign to Wie
for each Wte do
Coun Wellet ()) R(() (T.w Tw) 5) Noo.nNoo.Wallii Walls 12) T where R(x)
no, of Samples he Removed from Cluster. end for
else
Sample well patches 2y : NW & &f kij.
end
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
3.3. Satellite Imagery Acquisition & Label Creation
We used PlanetScope-4-Band imagery (PBC, 2024) fea- turing RGB and Near Infrared bands to represent satellite images of the region with medium resolution of about 3 meters per pixel. PlanetScope, product of Planet Labs, consists of approximately 130 satellites that can image the entire Earth's 's land surface daily, collecting up to 200 million sq. km of data each day. We obtained Surface Reflectance imagery, which is offset-corrected, flat-field- corrected, ortho-rectified, visually processed, and radio- metrically corrected. These processes ensure consistency across varying atmospheric conditions and minimize uncer- tainty in spectral response over time and location, making the data ideal for various applications.
We choose Planet Labs data over other alternatives since it updated daily, making it possible to pick a consistent time for all the images, which is important for training dataset consistency. It also provides multispectral imagery (4-band: RGB+Neaar Infrared), and the Near Infrared band is a useful addition (as we later verify in Table 7) since certain features, like ground depressions indicating well sites, may be more detectable in this band. Lastly, while other alternatives may be limited in remote regions, Planet's global satellite constellation ensures more consistent coverage. All the imagery we use is made publicly available in the dataset. To ensure the highest quality, we selected images with no cloud cover. The images were acquired by Planet satellites within a timeframe that aligns with the well-location data from AER. We obtained satellite images for each sample based on geographical coordinates, ensuring an intersection between the actual area of interest and the acquired imagery.
Figure 3. Sample image patches from dataset includes exam- ples with no wells, two wells, and multiple wells. Additionally, we present qualitative results with predictions generated by our Segmentation U-Net (ResNet50) and DETR ResNet50 model.
We frame the task of identifying wells both object de- tection and segmentation task. In real-world setting, high performance in either detection or segmentation would be sufficient for detection of wells; therefore we test both fram- ings since is not a priori clear which may be best suited to remote sensing algorithms. For each image patch, as shown in Figure 3, generated corresponding segmentation maps and object detection annotations for all known wells in the image based on the point labels provided in the AER data. For binary segmentation, we annotated each well site with a circle to match the teardrop shape typical for well sites. We standardized the diameter of a well site to a value of 90 meters (such sites typically range from 70 to 120 meters in diameter). We used the same scale define bounding boxes in the object detection task, following the COCO (Lin et al., 2014) format for annotations. The overlap in ground truth bounding boxes for some of the wells in Figures 304, and 11 reflects the clustering of multiple wells in densely developed oil and gas sites, where the spatial overlap of wells and infrastructure is common. (Note that this is a char- acteristic of the data, not a limitation of our quality control
strategy.) Additionally, we created multi-class segmentation maps, where each class represents a different state of the well (active, suspended, or abandoned), and included this information in the object detection annotations. (We do not perform multi-class segmentation experiments here, but it is possible that future researchers may find this task useful.)
4. Experiments
We evaluate the performance of deep learning models for binary segmentation and object detection tasks. Our focus includes all oil and gas wells, regardless of their operational status, since they exhibit similar footprints and consistent features, making them detectable satellite imagery.
We augment images by randomly resizing images to 256, ensuring all bounding boxes remain intact for ob- ject detection. We then apply horizontal and vertical flipping with a probability of 0.25 each, followed by normalization using channel-wise mean and standard deviation calculated from the training split of the dataset. The hyperparameters we use in these various models represent standard perfor-
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
mant settings and not intended to represent the outcome of hyperparameter optimization.
further insight into segmentation accuracy. FI-Score, the harmonic mean of precision and recall, provides balanced measure considering both false positives and false negatives.
4.1. Binary Segmentation
4.2. Object Detection
We selected well-known baseline models for binary seg- mentation, encompassing the deep CNN-based approaches U-Net, PAN, and DeepLabV3+, as well as the Transformer- based architectures Segformer and UperNet.
For binary object detection, consider both single-stage, i.., RetinaNet (Lin et al., 2017), FCOS and SSD, and two- stage CNN-based architectures, i.e. Faster R-CNN (Ren et al., 2017), and the transformer-based architecture DETR (Carion et al., 2020). RetinaNet (Lin et al., 2017) is a one- stage architecture trained using focal loss, which helps to address class imbalance. It uses Feature Pyramid Network for multi-scale feature extraction and efficient object detec- tion across different scales. Faster R-CNN (Ren et al., 2017) is a two-stage model recognized for its high accuracy. It employs a Region Proposal Network for generating region proposals and a separate network for predicting class labels and refining bounding box coordinates. FCOS (Fully Con- volutional One-Stage Object Detection) (Tian et al., 2019) directly predicts object locations and categories from feature maps, which is effective for small object detection. SSD (Single Shot MultiBox Detector) (Liu et al., 2016) uses mul- tiple feature maps at different scales, enhancing its accuracy for small objects. DETR (DEtection TRansformers) is a transformer-basee model that treats object detection as a set prediction problem. It eliminates the need for special- ized components such as anchor boxes and NMS, using transformers to directly predict the final set of detections.
U-Net (Ronneberger et al., 2015) was chosen for its widespread use as a baseline, offering an effective encoder- decoder architecture for multi-scale feature extraction. PAN (Li et al., 2018) improves multi-scale context with pyramid pooling and attention mechanisms. DeepLabV3++ (Chen et al., 2018) was selected for its popularity in remote sens- ing tasks with its Atrous Convolution and ASPP module for capturing contextual information at various scales. Seg- Former (Xie et al., 2021) is a transformer-basee architecture designed for semantic segmentation, utilizing self-attention mechanisms for capturing long-range dependencies. Uper- Net (Xiao et al., 2018) combines UNet and PSPNet (Zhao et al., 2017) architectures, featuring a UNet-like structure for multi-scale feature fusion and PSPNet's pyramid pool- ing module integrated with a Swin Transformer (Liu et al., 2021) backbone for efficient multi-scale processing.
We train all CNN-based models using a ResNet50 back- bone, batch size of 128, and the BCELogits loss func- tion. To fine-tune the model, cosine annealing sched- uler (Loshchilov& & Hutter, 2017) is used, which adjusts the learning rate smoothly in a cyclical manner by gradu- ally decreasing it. We also experimented with additional backbones with U-Net to evaluate the impact of backbones with larger receptive fields and attention mechanisms. This included ResNeXt50 (Xie et al., 2017), which enhances fea- ture learning through grouped convolutions; SE-ResNee550 (Hu et al., 2018), which introduces channel-wise attention with Squeeze-and- -xcitiiitttiion blocks; and EfficientNetB6 (Tan & Le. 2019), known for its balanced scaling. In seg- mentation, the combination of UperNet's ' contextual aggre- gation and the ConvNeXt backbone's s efficient representa- tion enables precise segmentation, particularly valuable for models,
All object detection models are trained using a ResNet50 backbone, except for SSD Lite, which is trained with a Mo- bileNet backbone. The batch size is set to 256 for Faster R-CNN and FCOS and 512 for RetinaNet and SSD Lite. We used a cosine annealing scheduler (Loshchilov & Hutter, 2017) 7) and trained all models for 120 epochs. DETR simi- larly uses ResNet50 backbone but has a batch size set to 64. All models are optimized using the Adam optimizer. For binary object detection model evaluation, we calculate Intersection over Union (IoU) at various thresholds (e.g., IoUp. IoU0.3. IoUn.5), which measures how well predicted bounding boxes align with ground truth. IoU is computed by dividing the area of overlap by the area of their union, with higher values indicating better alignment. IoU thresh- olds define the minimum overlap required for predicted box to match ground truth box. (For example, an IoUo. threshold means a predicted box must have at least 50% overlap with a ground truth box to be considered a correct detection.) We also assess Mean Average Precision (mAP), including mAP30 and mAP50.95. measuring the model's precision-recall trade-off and detection accuracy at various IoU thresholds. mAP30 measures precision at an loU thresh- old of 0.5, while mAPs0-99 averages precision across IoU thresholds from 0.5 to 0.95. Higher mAp scores reflect better detection accuracy and precision. While higher IoU
remote sensing applications. For transformer- while both Segformer and UperNet use Dice loss function and polynomial learning rate scheduler, Segformer utilizes a mit-bO-ade (Xie et al., 2021) backbone with a batch size of 128, while UperNet uses ConvNeXT (small and base) and Swin Transformer backbones with a batch size of 64. All models are optimized using AdamW for 50 epochs.
We evaluate the binary segmentation task with respect to IoU, Precision, Recall, and F1-Score. High Precision cor- responds to reducing false positives, while high Recall cor- responds to reducing false negatives. IoU measures the overlap between predicted and ground truth masks, offering
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Table Results for the binary segmentation task for variety of models evaluated over the set. We report the Intersection over Union (IoU), precision, recall, and Florcore.
Archisecture
Backbone
Params
GFLOPs
IoU
Score
Precision
Recall
ResNet50
32,52M
21,42
5840.5
61.9=088
90.2:12.2
6..321.6
ResNext50
32M
21.81
58.2+0.22
62.1=0.3
88 24:35
63.6+1.7
U-Net
SE ResNet50
35,06M
20.83
58.9220.7
62.9=0.7
88.8+11.6
64.4+1144
N0lccenNNe1B66
43.83M
6..4
6..8.0.4
878100
66.31.0.
PAN
24.26M
17.47
57.8:10.8
61.5=099
1..
61.5:0.9
D3+
ResNet50
26,68M
18.44
56.8:10.7
6..6
89.4.1..
618811111
Segformer
mit. ade
3.72M
3.52
57.6:+0.5
61.3-20.6
82.642299
69.242211
ConyNexT-Small
128. 29M
81.76
59.4+00.1
63.5=0.1
81
71.5+0044
UperNet
ConvNexT-Base
146.27M
121.99
59.7++0.3
63.8=022
81.1-0077
72.2+0022
swin small
81.15M
134,20
59.9:10.7
64.2=007
80.6.0.5
73.1::0.1
Table 5. Results for the object detection task for a variety of models evaluated over the test set. We report the intersection over union (IoU) 0.3,
over thresholds 0.3, 0.5 and the mean average precision (mAP) for both IoU= 0.5 and IoUE [0.5, 0.95] thresholds.
Architecture
Backhone
Params
GFL.OPs
InUoo.1
Tolio
Inllo.s
mAPs0
mAP50.95
RetinaNet
18.87M
0.93
24.58 0.11
43.07.1088
09.79.10.36
0.1811..28
0.63:11112
FasterRCNN
ResNet50
41.09M
24.7
3.079-1.007
46.95.10.66
61.29:10.35
5.20:11.00
19.12:13.41
FCOS Lite
31.85M
25.81
34.79=10.99
48,51
62.66:20.43
9.67#1447
30.46:2.111
MobileNet
3.71M
0.64
33.91=0.18
50.30:10.08
65.07:00.03
9.76:00.39
25.14:1:0.6
DETR
ResNet-50
41.47M
6.86
41.78=0.11
51.15-0.114
63. 63.17:0.11
15.22-20.8
38.45-10..2
values indicate better accuracy for individual predictions, mAP provides broader measure of detection performance by capturing precision across different IoU criteria.
the ground truth, is crucial. However, high recall value ensures the model captures most actual well instances, re- ducing the risk of missing important information. Thus, the Uper-Net model with the highest recall value of 73.1 = 0.1, which excels at capturing global context information, ap- pears a good candidate for this task, given a decent precision score. Taking into account both precision and recall, U-Net with EfficientNetB6 backbone performs well, suggesting the utility of a larger backbone with a bigger receptive field. However, ConvNeXt Small provides trade-off between computational efficiency and performance, requiring fewer GFLOPs and parameters compared to UperNet with Swin Small while achieving similar performance.
5. Results
Our tasks involve identifying a roughly circular well region with a 90m diameter in real life, which translates to less than 30 pixels in satellite imagery due to resizing and other augmentations. This poses challenge for machine learning models given the heterogeneous nature of the background, including various similarly shaped and sized features of the natural and built environment. Additionally, vegetation can occlude wells RGB channels, highlighting the importance of hear-infrared imagery for guidance. The wells the also vary somewhat in shape and can be in various states of disrepair as result of differing ages and maintenance.
5.2. Binary Object Detection
Our evaluation, as shown in Table 5, indicates that while all models perform reasonably well in terms of aligning pre- dicted and actual well locations, performance in the object detection task overall lower than for segmentation indi- cating that potentially segmentation is simply better fram- ing for this task in real-world settings. The observed gap in performance is likely attributed to the inherent challenge of detecting the small-sized wells within the satellite imagery. It is well-known that single-stage CNN architectures (such as FCOS and SSD) often demonstrate better performance on small object detection than two-stage methods (such as Faster R-CNN), and this aligns with our observations. The exception here is the single-stage method RetinaNet, which, although it has comparable loU scores, struggles to detect wells accurately. SSD Lite stands out with the highest ToUn.5 score of 65.07 + 0.03 and loUn.3 score of 50.3 + 0.08. Whereas all models are quite similar in terms of IoUg. the highest score by FasterRCNN is 36. 79 + 1.07.
5.1. Binary Segmentation
For the binary segmentation task framing, we train Mod- els (from scratch) using both CNN-based and Transformer- based backbones, considering the prevalent imbalance in the image data due to the small size of wells. Although we did use 3-dimensional, ImageNet initialized weights of the back- bone but modificd the initial layers afterwards to support 4-dimensional multispectral images. Among our models, as shown in Table the traditional U-Net with EfficientNetB66 backbone performs the best, with CNN-based models show- ing the highest IOU of 60. 0.3 and F1-Score of 64.8 While ResNet50- basdbbee- backbone achieves the highest Pre- cision of 90.2 + 2.2, indicating more accurate predictions of well instances compared to other models. Precision, which reflects the accuracy of our positive detections compared to
7

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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Table Results for object detection task for the FCOS Model with ResNet50 backhone, trained with and without NTR Imagery.
Modality 1 RGB+NIR
ToDg.a
ToUlo.5
mAPso
mAP5a.95 30.46
04.79:10.99
0.55 46, 26.00
6,.66:10.43 61.23:+:1.58
9.67:11477
RGB 32.39+22888
5.7=3.65
20.00=10.44
Table Results for the binary segmentation task for U-Net with ResNet50 backbone, trained with and without NIR imagery.
Table 8. Performance comparison of U-Net with ResNet50 back- bone for binary segmentation trained on active wells only versus all well types. Our results demonstrate that training merely on active wells is not sufficient to detect abandoned and suspended wells in practice highlighting the importance of including all
Modality RGB+NIR 58,00=0.50
Score
Precision 20.20=2.20
Rocall
61.940800
62,30 1,00
RGB 56.60=0.44
60.50++0.35
87.00=
62.54:10.13
three types in the dataset.
Metric
Train Set (Well Type Label Present)
Text Set
Thus, SSD Lite and FCOS excel in localization, especially at higher IoU thresholds, while Faster R-CNN is adept at detecting objects with minimal overlap. All models demon- strate low performance in terms of mAPso. which assesses precision-recall trade-off and detection accuracy at an IoU threshold of 0.5. FCOS achieves the of 9.67 = 1.47 while SSD Lite achieves a score of 9.76 0.39. This may be due to these models not producing region proposals confidently enough, especially in instances with a large number of wells. Whereas over a broader evaluation with mAP50:95 which av- erages precision across IoU thresholds from 0.5 to 0.95. All models apart from RetinaNet provide much better results, with FCOS achieving the highest score of 30.46++3.11 11, indi- cating a decent performance in the identificatinn of well in- stances. Most notably, the transformer- based model DETR demonstrates the strongest overall performance, achieving strong IoU scores across thresholds and the highest mAP50 (15.22 28) and MAP50: 95 (38. 0..32) scores. These results highlight DETR's 's ability to effectively capture global contextual cues essential for accurate well detection.
Active
0.502
ToUl
All (T+IeeII
0.576
Active
0.503
Fl Score
All (T+IleIIT)
0.614
Precision
Active (I)
0,998
All (TeITeIIT)
0.913
Active (I)
0.502
Recall
All (++I++1I))
0.614
better performance in both tasks.
Benefits of Using Multiple Well Types. While much past work has focused on active wells, we show that training on such data is insufficient to detect abandoned and suspended wells (which are often significantly harder to e.g. degra- dation and vegetation growth over time). The results in Table 8 show that including all well types significantly out- performs training on only active wells, improving IoU, Fl score, and recall. Recall in particular is key for monitor- ing, where missing abandoned wells is more important than erroneously flagging a location as a well.
5.3. Value Added from Dataset Breadth
6. Limitations
In these experiments, we consider the value provided by our inclusion of (i) near-infrared (NIR) imagery addition to RGB channels, and (ii) the inclusion of abandoned and suspended wells in addition to active wells.
We do not envision any significant negative uses of our work. Localization of wells is primarily of interest to the climate change mitigation community and is not, for example, a primary means whereby fossil fuel companies select new locations for drilling. Therefore, do not believe this dataset is susceptible to dual use risks.
Benefits of NIR imagery: We find that including the NIR band significantly enhances both object detection and seg- mentation performance compared to purely RGB data. In segmentation (Table 7), RGB+NIR outperforms RGB in IoU, F1 Score, and Precision while slightly lowering Re- call. A similar trend is observed in the object detection (Table 6) with RGB+NIR achieves higher Intersection over Union (IoU) scores across all thresholds (0.1, 0.3, and 0.5) and considerable increase in mAP@50 and mAP@ 50:95 These improvements can be attributed to the enhanced spectral information provided by the NIR band, which is particularly effective in detecting features such as ground depressions that may indicate well sites. These features are often more distinguishable in the NIR spectrum, leading to
One potential limitation of our work is that we rely on well locations listed by the Alberta Energy Regulator. It is likely that some true well locations are missing in this data, leading to the potential for false negatives in the ground- truth data for this problem. However, it be expected that this will not significantly affect the training of algorithms since these labels represent small fraction of the negative locations in the dataset, and deep learning algorithms are known to be robust to moderate amounts of label noise (see e.g. Rolnick et al. (2017)). Instead the effect may simply be that the reported test accuracy is actually lower than the true value (due to certain correctly predicted well locations
8

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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
abandoned and suspended wells, which are significant source of greenhouse gases and other pollutants. We com- bine high-resolution imagery, an extensive database of well locations, and expert verification to create the Alberta Wells Dataset. We frame well identification both in terms of object detection and binary segmentation and evaluate the perfor- mance of a wide range of popular deep learning methods on these tasks.
We find that the UperNet Model (with ConvNexT back- bone), in particular, represents the most promising baseline for the binary segmentation task, while for object detection, all models demonstrate more mixed results, with Single Stage Models (such as FCOS and SSD) providing a rel- atively promising baseline. These results show that the Alberta Wells Dataset represents both challenging as well as societally impactful set of tasks.
The value added by the dataset is twofold. First, most global fossil fuel-producing regions do not have databases of well locations comparable to that provided by AER. Alberta's varied landscape provides an ideal setting for developing algorithms for the detection of wells, which can then be used directly in other locations fine-tuned. Second, even in Alberta, the list developed by AER is not comprehensive, and many abandoned wells are believed to be missing: re- mote sensing algorithms can provide candidate locations for domain experts to examine so as to determine the true number and locations of abandoned wells in Alberta.
Figure 4. few Sample image patches from our dataset presenting qualitative results with predictions of failure cases generated by our Segmentation U-Net and DETR with ResNet50 model.
being evaluated as false). We hope to investigate such effects further in future work.
Our dataset is focused on Alberta, because (1) it is very large region with a significant amount of high-quality la- beled data available, (2) it is one of the world's most impor- tant locations for oil and gas production, so identifying wells in Alberta is of immediate impact. Future works may wish to assess the capacity for few- or zero-shot transfer learning from Alberta to other regions with a high expected concen- tration of abandoned wells, including the Appalachian and Mountain West regions of the United States, as well as number of former Soviet states.
Overall, we hope that our work will make it possible to (i) better estimate the total effects of well-related methane emissions (currently extremely uncertain (Williams et al., 2020)) and groundwater contamination, (ii) prioritize wells to be plugged and decontaminated. We believe that the scalability of remote sensing will make it an invaluable tool in mitigating the global environmental impact of abandoned oil and gas wells.
While our initial results offer encouragement, a deeper ex- amination reveals a few failure cases arising from visually complex scenarios that highlight key challenges in our de- tection pipeline (as represented in Figure 4). Our model performs well in typical scenarios, where most patches con- tain only 1-5 wells. However, in rare high-density regions, performance can degrade- - leading to undercounting or missed detections. Abandoned and suspended wells also pose a challenge due to their subtle visual signatures and minimal surface infrastructure. While near-infrared bands help in some cases, spectral cues alone are not always suf- ficient. Improving detection in these visually complex and low-signal cases is a key focus for future work.
Software and Data
Alberta Wells Dataset is available at: https::/ http///corrs/11333303 Our Benchmarking Code is available at: https: attps://githubt com/ RolnickLab//////
A ChbertaWWell__Dttte
Acknowledgmentss and Disclosure of Funding
This work was funded in part through the Canada CIFAR AI Chairs program and a McGill Tomlinson Scientist Award. We acknowledge support from Planet Labs in the form of their academic subscription program, and are grateful for the help of Tim Elrick in working with Planet data. We also thank Mila and the Digital Research Alliance of Canada for provision of computing facilities, and NVIDIA Corporation
7. Conclusion
In this paper, we present the first large-scale benchmark dataset aimed at identifying oil and gas wells, with focus

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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
for additional computational resources. Special thanks to Francis Pelletier, whose feedback data collection pipeline and coding significantly improved methods.
213-229, Cham, 2020. Springer International Publishing. ISBN 978-3-030-58452-8.
Chang. H., Bai, L.,, Wang, 2., Wang, M., Zhang, Y., Tao, J., and Chen, L. Detection over-ground petroleum and gas pipelines from optical remote sensing images. In Remote Sensing. 2023.
Impact Statement
This work presents the first large-scale machine learning benchmark for pinpointing onshore oil and gas wells, includ- ing abandoned, suspended, and active wells. Our dataset, the Alberta Wells Dataset, contains over 200,000 well in- stances and high-resolution satellite imagery, enabling ad- vancements in the identification of oil and gas wells. By framing the problem as one of object detection and binary segmentation, we hope this dataset will help improve the scalability and accuracy of well detection techniques.
Chen, L.-C.,, Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European Conference on Computer Vision (ECCV), September 2018.
ECCC.
Greenhouse gas
emissions.
ensitrommennt--lllttteehhaaggg
Abandoned wells, particularly those that leak methane and other pollutants. pose major environmental challenge. By improving methods for detecting these wells, this work aims to reduce their environmental impact by identifying high- emission sites that can be prioritized for remediation. This can contribute to mitigating climate change by helping to reduce harmful emissions from these otherwise difficult-to- detect sources.
services/eaviiooonnntaa----dddee greenhouse-gas-eeiee [Accessed 28-09-2024).
html,
2024.
Guisiano, J. E., Moulines, É., Lauvaux, T., and Sublime, J. Oil and gas automatic infrastructure mapping: Leverag- ing high-resolution satellite imagery through fine-tuning object detection models. Luo, B., Cheng, L., Wu, Z.-G., Li, H., and Li, C. (eds.), Neural Information Pro- cessing, pp. 442-458, Singapore, 2024. Springer Nature Singapore. ISBN 978-981-99-8144 -99-8148-9.
To enable wider responsible uses of the dataset, we will release it under non-commercial usage license, making available for academic and research purposes only. We do not foresee significant negative impacts arising from this work. Dual uses the algorithms we outline here are limited, and the algorithms used are comparatively small- scale and have low energy and carbon footprint.
Hu, J., Shen, L.,, and Sun, G. Squeeze-and works. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132-7141, 2018. doi: 10.1109/CVPR.2088.000
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
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A. Hosting, licensing, and maintenance plan
A.1. Hosting & Maintenance
We are hosting the dataset on Zenodo at
A.2. Data Licensing
The AWD Dataset is released under Creative Commons Attrbution-NonCComeriiic 4.0 International (CC BY-NC 4.0) hetp///ieeee////////////
License (https: http://creaiiiiiiee iecrrmmoiss
The satellite imagery for this project was acquired through Planet Labs' (PBC, 2024) Education & Research license, which allows the use of the data in publications and the creation of derivative products related those publications. However, the raw imagery cannot be shared publicly. To adhere these guidelines, we provide the data in HDF5 format, with the satellite imagery pre-processed to produce derived product represented numpy array from Raster Vector. This process removes all geographic metadata.
This data is for academic use only and should not be used commercially. Proper credit to the current authors, Planet Labs (PBC, 2024), and the Alberta Energy Regulator (AER, 2024) is required when using this data.
B. Dataset Information
The purpose of this dataset is to assist in training deep learning systems identify oil and gas wells, including abandoned, suspended, and active ones. This will enable the detection of wells in specific area, allowing comparison with government records. If discrepancies are found, experts can conduct further investigations, which can possibly lead to the discovery of an abandoned or suspended device that might not be present in government records.
B.1. Dataset Structure
We provide training, validation, and testing sets, split using our proposed algorithm (as described in Section 3.2 of the main paper) to create a well-distributed dataset. The proposed method aims to create smaller regions of well concentration by clustering the centroids of patches. These regions are designed to be (a) mutually non-intersecting, (b) part of larger geographic region by clustering the centroids of the initial clusters, and (c) containing a similar distribution of non-well patches within the same region.
This approach ensures that the training, validation, and test sets include representations from all geographic regions, providing diverse and comprehensive evaluation. Thus, the dataset represents various geographical regions and offers a diverse benchmark for evaluation and testing. Each dataset split is saved in an HDF5 format file, structured as described in the following sections, and then compressed into a .targzz file for faster transfer.
Details on the number of samples in each set and the size of the dataset, both original and compressed, are presented in Table
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
B.2. Dataset File Directory Structure
The following directory structure is used for each dataset file being stored in Hierarchical Data Format 5 (HDF5) file:
(Train/estt/all>See
image
sample <ample_name>> name>
Satellite Image (Multispectral Rasterio Image Data)
Meta Data <sample_nane name>
label
Seinary_seg_maee
---BBinary Segmentation Map (Rasterio Image Data)
mu couuti_clln mmlli_class_se__mppp
Mullillasssss Segmentation Map (Rasterio Image Data)
bounding_boog annotations
--- samppleem___me
Bounding Box JSON Data (COCO Format)
author Anonymous Author (s)
descriptioonn Alberta Wells Dataset: Pinpointing 0il and Gas Wells
from Satellite Imagery
B.3. Structure of Dataset Directory
To enhance the efficiency of the data loader, we split the larger .I5 dataset into smaller .h5 files, each corresponding to a unique sample (image patch). By splitting the dataset in such a manner, we are able to improve the speed per iteration the dataloader by over 100%.
This results in the following data structure:
<Sample Id> .5
image
Satellite Image (Multispectral Rasterio Image Data)
Meta Data
label
-bnary_seg_mapp
-
--i-Binary Segmentation Map (Rasterio Image Data)
mu
-- -MMMMMcllllssss Segmentation Map (Rasterio Image Data)
Bounding Box JSON Data (COCO Format)
:uthor::nonyyous Author
description: Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
B.4. Dataset Size Distribution of Samples
Our dataset comprises over 94,000 patches of satellite imagery containing wells, with total of 188,000 patches sourced from Planet Labs (PBC, 2024), covering more than 213,000 individual wells. Details about the distribution of the number of patches, wells present, and dataset split sizes are provided in Table 9, with the distribution of the number of wells sample being described in Table 10. We also include an equal number of images that contain no wells in each dataset split. The distribution of wells per sample, along with the corresponding number of wells and the breakdown of well types, is illustrated in Figure?? 7 and detailed in Tables 11, 12, and 13. The geographic distribution wells in the dataset can be visualized Figure 6.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Table Dataset statistics represented the various splits of the dataset.
Dataset
No of
No of Wells
Original HDF5
Compressed targzz
Split
Samples
in Split
File Size (in Gb)
File Size (in Gb)
Train
167436
194231
322
100
Validation
9463
8243
19
5.7
Test 11789
10973
24
7.1
Total
188688
213447
365
112.8
Densily Map Using Hexagonal
Longtude
Figure 6. Density map of wells in the Alberta Wells Dataset.
Table 10. The distribution of individual wells in positive samples from the dataset. We also include an equal number of images that contain no wells each dataset split.
No Wells Sample
Frequency of Well Instances in a Sample
Training Split
Validation Split
Test Split
44299
3393
4128
2-3
25378
979
1242
4-5
7899
190
328
6-10 10
4927
123
227
11-15
751
23
38
16
333
11
19
26-35
67
10
2
36
45
3
0
56-75
18
0
0
76 125
0
0
Total
83718
4732
5984
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
10200
59140
Non
50303
Frequency Semplles
20546
35333
20002
9945
8
8
No Welks
Training
35:0
Type
Non Wel
Active Well
Suupeended
Abonndon Wolks
2000
Trequencyy
35:0
1000
R24
910
of Wells
(b) Validation set
Wel Type
4030
Wen-Well
Active Wets
Sugpended Well
Ab donded Welk.
5i5mmples
3050
2858
25:0
2000
1862
35:0
520
352
16-22
(c) Test set
Figure 7. Distribution of the number of individual wells and the proportion of well types (active, suspended, and abandoned) positive samples from the dataset. We also include equal number of images with wells all.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Table 11. Test statistics showing the distribution of image samples by the number of wells per image and the breakdown of well types (active, suspended, and abandoned).
No of
Test Set
Wells in
Count
Distribution of Well Type in Samples
a Image Sample (S)
(Image
Total
Active
Suspended
Abandoned
Samples)
Wells
Wells
Wells
Wells
4128
4128
999
1325
1804
2-3
1242
2858
1042
844
972
4-5
328
1445
495
464
486
6 10
227
1662
604
555
503
15
38
471
144
181
146
16-25 25
19
352
56
184
112
26 35
2
57
0
56
10973
3340
3609
4024
Table 12. Validation Set statistics showing the distribution of image samples by the number of wells per image and the breakdown of well types (active, suspended, and abandoned).
No of
Validation Set
Wells in
Count
Distribution of Well Type Samples
Image
(Image
Total
Active
Suspended
Abandoned
Sample (S)
Samples)
Wells
Wells
Wells
Wells
3393
3393
743
1225
1425
2
979
2197
833
654
710
5-5
190
824
346
248
230
6 10
123
910
323
331
256
11-15
287
67
114
106
25
11
204
32
63
109
26
10
300
33
22
245
36
3
128
29
14
85
8243
2406
2671
3166
Table 13. Train Set statistics showing the distrihution of image samples by the number of wells per image and the breakdown of well types (active, suspended, and abandoned).
No of
Train Set
Wells in Image Sample (S)
Count
Distribution of Well Type in Samples
(Image
Total
Active Wells
Suspended
Abandoned Wells
Samples)
Wells
Wells
44299
44299
16715
13116
14468
2-3
25378
59190
33099
12706
13385
4-5
7899
34544
22456
6321
5767
6- 10
4927
35333
21522
7796
6015
11-15
751
9345
4076
3136
2133
16-25
333
6381
1781
2544
2056
26-35
67
1994
376
791
827
36 55
45
1929
172
777
980
56
18
1129
86
345
698
76 125
1
87
11
63
13
83718
194231
100294
47595
46342
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
bear sholk Bend
Carp Bond
Bet card
(a) A sample patch with Bhox annotations and the corresponding imagery its different spectral bands.
(b) sample patch with its segmentation labels (binary and multi-class) and bound- ing box annotations.
Figure 8. Sample Patch from the Evaluation Set with 2 active wells.
B.5. PlanetScope Satellite Imagery
For our experiments, we selected a 4-band (RGBN) satellite imagery product (ortho analytic.4b (bbyttic..bssr) from Planet Labs (PBC, 2024) illustrated in Figure This product uses Planet's PSB.SD instrument, which features telescope with larger 47-megapixel sensor and is designed to be interoperable with Sentinel-2 imagery in several bands. The frequency of each band of image is described in Table 14. The instrument provides a frame size of 32.5 km 19.6 km, image capture capacity of 200 million km²/day, and an imagery bit depth of 12-bit, with ground sample distance (nadir) ranging from 3.7 m to 4.2 m.
The satellite images are corrected for atmospheric conditions and spectral response consistency. These multispectral products are tailored for monitoring in agriculture and forestry, offering precise geolocation and cartographic projection, They are ideal for tasks such land cover classification, with radiometric corrections ensuring accurate data transformation.
Table 14. The Frequency of Each Spectral Band a Planctscope PS.SD acquired Image
Band of Image
Frequency (in nm) of Spectral Band
Band Blue
465 515
Band = Green
547 585
Band 3 = Red
650 680
Band 4 Near-infrared
845 885
B.6. Meta Data Description
Each dataset sample is accompanied by metadata, including the sample name (sample ID in string format), the presence of a well in the sample, the number of wells in the sample, and whether well of a specific category present in the sample. Table 15 provides illustration of metadata associated with sample.
Table Sample of Meta- Data Associated with each Instance the Dataset
Meta-Dait Attribute Name
Value eval_6933
Sample_Nas Name wells present no of wells
Truc
10
Abandoned. www.wwllllpeeeeee present
True
Active stttivewwlllppeseeee present Suspended. well. present
True
presspt
True
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
B.7. Label Data Deseription
For our experiments, we create single-channel segmentation maps, which are binary maps used to locate instances of wells. We also generate multi-class segmentation maps, where each class denotes well in active, abandoned, suspended state. Furthermore, provide COCO format object detection labels for wells. In both segmentation and detection labels, we represent various states with class IDs as "Active": 1, "Suspended': 2. Abandoned': 3. To maintain consistency, we standardize the diameter of a well site to 90 meters (typically ranging from 70 to 120 meters) when annotating, resulting in 30-pixel diameter in the labels. Figure 8(b) illustrates image patches with their corresponding labels, Figure 8(a) illustrates various spectral bands present in an image alongside the original image with bounding box annotations for reference and an example of bounding box label in COCO format is shown below.
Sample of Bounding Box Annotation:
{
id 0, immaeeiid : evval_002
category id 1, 'bbox": [46, 145, 29, 291, "iscrowd".
{
id : 1,
iimageiid : '0val_7028',
category id : 2,
'bbox [45, 127, 29, 29]. "iscrowd"
C. Dataset Samples Illustration
Samples from the dataset, covering various scenarios, are shown in Figures and 10. A few more samples with prediction of the models and labels for binary segmentation and binary detection is illustrated in Figure 11.
D. Challenges for ML Community
The Alberta Wells Dataset presents several intriguing challenges for machine learning. Key issues include an imbalanced data distribution, with fewer instances areas with multiple wells compared to those with single or two wells, and the visual similarity among active, suspended, and abandoned wells, which can confuse standard models. Additionally, varying spatial relationships in the imagery due to varying geography create difficulties for off-the-shelf models. Noise in annotations, even after data quality control and cleaning such as misclassified furtherurther complicates the task. Despite these challenges, the dataset's large scale and geographical diversity, covering over 213,000 wells, offer significant opportunities for developing robust and generalizable ML models for monitoring oil and gas infrastructure.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Cendetege with Intobatine
Leer Intraed
Green Bard
Red
Image with Inrotwiioes
Green Band
Oue Rand
Red Band
Image with Arrolation
hoar Infored
Dramn Band
with Arrctations
Mear Infrared Band
Grean Band
Band
Red Band
Image with Ponotatiine
Near Infraed
Grean fand
Due
Red Dand
with Arrosationa
Mear arhaned Band
Dresn
Band
Sund
Figure 9. Qualitative results from the dataset illustrate the diverse distribution of wells in dataset samples, including Bbox annotations and corresponding imagery in different spectral bands.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Eanry Lmbdann
K.L. www.ehaellee
Eondin Arerstrr
Pordin Sreddie
Bran Copeedalis
Bavin Sqaeiiiinnikk
Figure The qualitative results from dataset showcase the varied distribution of wells dataset samples, with their corresponding segmentation labels (binary and multi-class) and Bbox annotations.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Figure Few sample image patches our dataset includes examples no wells, two wells, multiple wells. Additionally, we present qualitative results with predictions generated by our Segmentation U-Net (ResNet50) and DETR ResNet50 model.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
E. Documentation frameworks: Datasheet for Datasets
E.1. Motivation
1. For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide description.
The Alberta Wells Dataset (AWD) was created to identify oil and gas wells-whether abandoned, suspended, or active-using medium-resolution multi-spectral satellite imagery. While the issue of detecting oil and gas wells has been addressed by several authors, existing datasets are typically small (500-5,000 samples) and limited to specific regions, often including only active wells. This limitation reduces their effectiveness in identifying abandoned or suspended wells. The AWD aims to fill this gap in the literature by offering a comprehensive dataset with over 188,000 samples (including over 94,000 samples containing wells) from PlanetLaabs satellite imagery, encompassing more than 213,000 individual wells.
2. Who ereated the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?
The raw data is sourced from the Alberta Energy Regulator (AER), specifically from the monthly AER ST37 publication. This dataset includes comprehensive details about all reported wells in Alberta, such their geographic location, mode of operation, license status, and the type of product extracted, among other attributes. The data is provided in shapefile format along with accompanying metadata. However, the dataset cannot be used directly because the license status or mode of operation often does not reflect the well's actual status. Therefore, the authors include domain experts, who specialize in field measurements of methane and air pollutant emissions from oil, gas, and urban systems, as well as in the geospatial and statistical data analysis of emissions and energy infrastructure, to ensure the quality of the dataset.
3.
Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. This work was funded in part through the Canada CIFAR AI Chairs program and a McGill Tomlinson Scientist Award. We acknowledge support from Planet Labs in the form of their academic subscription program, and are grateful for the help of Tim Elrick in working with Planet data. We also thank Mila and the Digital Research Alliance of Canada for provision of computing facilities, and NVIDIA Corporation for additional computational resources.
E.2. Composition
What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide description.
We provide dataset file stored in Hierarchical Data Format 5 (HDF5, i.e., .h5 file), which contains multispectral 4-band RGBN satellite images in raster format and data labels with both identified by unique instance names. These satellite images, acquired from Planet Labs, have a resolution of 3 meters per pixel and include corresponding metadata. The metadata contains information about the number and types of wells present in patch. For data labels, we offer binary segmentation maps, multi-class segmentation maps (each class representing a well in an active, abandoned, or suspended state), and COCO format object detection labels. The images were taken from the province of Alberta, Canada, with each satellite imagery patch representing square with a side length of 1050 meters (1.05 km), covering an area of 1.025 square kilometers. The entire dataset spans over 193,000 square kilometers.
How many instances are there in total (of each type, if appropriate)?
The proposed dataset comprises 188,688 instances, of which 94,344 contain one or more wells, totaling 213,447 well points. Each instance includes corresponding multispectral satellite imagery, segmentation maps (both binary and multi-class, with classes indicating active, suspended, or abandoned states), and bounding box annotations with the state of operations as the object class ID in COCO format. We standardized the diameter of a well site to 90 meters (typically ranging from 70 to 120 meters) for creating annotations, resulting in a diameter of 30 pixels in the labels. More details about the distribution of wells in each split are provided in the supplementary materials as well as the main paper.
Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
representative of the larger set, please describe why not (e.g., to cover more diverse range of instances because instances were withheld or unavailable).
The AWD Dataset is based on the AER ST37 monthly status data of wells in the Alberta region of Canada. It includes wells that are in active, suspended, or abandoned states of operation. To ensure the dataset's 's quality, the authors with appropriate domain expertise conducted extensive quality control, filtering, and duplicate removal. This process was necessary because the full dataset included cases of well sites being restored and reclaimed, as well as various duplicates, noise, and data on other types of wells involving different natural resources. Therefore, the AWD Dataset, which includes multi-spectral satellite imagery, segmentation. and detection labels, is constructed from refined subset of the original AER ST37 data, specifically targeting oil and gas wells that can be precisely identified.
What data does each instance consist of? "Raw" data (e.g.. unprocessed text images) or features? In either case, please provide a description.
Each Image instance in our dataset, formatted in HDF5, contains satellite imagery represented as numpy array from Raster Vector. We preprocessed this imagery by reprojecting it to the EPSG 32611 I coordinate reference system and removed all geographic metadata, such as image bounds and coordinates, from the shared data. However, we do provide attributes like Sample Name, wells present, no of wells, Abandoned well present, Active well present, and Suspended well present. We utilized Planet Labs' 4-band (RGBN) satellite imagery product (ortho analytic 4b sr), which incorporates the latest PSB.SD instrument with 47-megapixel sensor. Each satellite imagery patch acquired represents square with side length of 1050 meters (1.05 km), covering area of 1.025 square kilometers. The entire dataset spans over 193,000 square kilometers.
Is there a label or target associated with each instance? If so, please provide a description. There are three types of labeled data for each image: binary segmentation maps (in Rasterio Image format) indicating the presence or absence of oil and gas wells, multiclass segmentation maps (also in Rasterio Image format) potentially identifying various classes of objects, and bounding box annotations (in COCO format) specifying the location and size of objects, such as wells, within the image. These components together form a comprehensive dataset suitable for training and evaluating machine learning models for tasks like object detection and segmentation in satellite imagery, partieularly focused on pinpointing oil and gas wells in Alberta
Is any information missing from individual instances? If so, please provide description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information but might include, e.g., redacted text.
The satellite imagery used in this project was obtained under Planet Labs' (PBC, 2024) Education & Research license, which prohibits sharing raw satellite imagery. We re-projected the raw data to EPSG:3261 1 using the nearest resampling method and removed all geographic metadata, such as image bounds and coordinates, from the shared data imagery to create a derived product that complies with the license terms.
Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)? so, please describe how these relationships are made explicit.
N/A
Are there recommended data splits (e.g., training, development/vaidattion,, testing)? If so, please provide description of these splits, explaining the rationale behind them.
The dataset we propose comprises more than 94,000 patches of satellite imagery containing wells, totaling 188,000 patches sourced from Planet Labs. This dataset covers over 213,000 individual wells. To ensure a balanced dataset, we divided it into training, validation, and testing sets using our algorithm outlined in Section 3.2 of the main paper. Our proposed method for splitting the data aims to create smaller, non-overlapping regions of concentrated wells by clustering patch centroids. These regions are intended to (a) not intersect, (b) be part of larger geographic area by clustering initial cluster centroids, and (c) contain a similar distribution of non-well patches. This approach ensures that the training, validation, and test sets cover all geographic regions, providing diverse and thorough evaluation. The dataset splits represent various geographical areas, making it a comprehensive benchmark for evaluation and testing. Each dataset split is stored in an HDF5 format file.
Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. One limitation of our study is our reliance on well locations provided by the Alberta Energy Regulator, which may not encompass all sites, leading to potential omissions in the ground-truth data. This could result in a lower reported validation and test accuracy, with some correctly predicted well locations being mistakenly categorized as false.
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Is the dataset self-contained, or does it link to otherwise rely on external resources (e.g,, websites, tweets, other datasets)? If it links or relies external resources, a) are there guarantees that they will exist and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide deseriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate. The dataset does not rely on the persistence of external resources.
Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege by doctor-pe doctor-patient confidentiality, data that includes the content of individuals' non-public communications)? If so, please provide description. No.
Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.
No.
E.3. Collection Process
How was the data associated with each instance acquired? Was the data directly observable (c.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g.. part-of-speech tags, model-based guesses for age or language)? If the data was indirectly inferred/derived from other data, was the data validated/verffied? If so, please describe how.
The AER publishes AER ST37, a monthly list of wells in Alberta, including location, operation mode, license status, and product type. However, the data needs rigorous quality control as license status, or operation mode may not accurately reflect the actual well status. The authors, with extensive domain expertise, removed duplicate well entries in the metadata and shapefile, keeping the most recent update. We then merge and filter the datasets, categorizing wells as active, abandoned, or suspended based on expert criteria. Duplicate coordinates are resolved by keeping the instance with the latest drill date. We verify all wells are within Alberta's 's boundaries. After thorough quality control by domain experts, we calculate the geographical bounds covered by wells and divide the region into non-overlapping square patches. These patches include varying numbers of wells, with an equal number of patches with and without wells.
What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated? We acquired multispectral satellite imagery data from Planet Labs, which comprises four bands (RGBN) with 3-meter-per-pixx resolution obtained through their proprietary API. This data was processed using quality-cottrolled and cleaned well data to generate segmentation and object detection annotations. The annotations were created using custom Python code, leveraging libraries like Shapely, GeoPandas, and Rasterio, and were validated through visualization using folium and matplotlib.
If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? No.
Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)?
The dataset was a collaborative effort involving the Alberta Energy Regulator, Planet Labs, and the authors. Without the contributions from individuals in these three organizations, this dataset would not have been possible. Proper credit must be given to the authors, Planet Labs, and the Alberta Energy Regulator when using this data.
Over what timeframe the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created.
We acquired the data from the Alberta Energy Regulator, specifically from its monthly well bulletin AER ST37 (AER, 2024), dated March 2024. Leveraging domain expertise, we filtered the data to reflect the condition of wells as of September 30, 2023. This decision was made because imagery acquired from Alberta during the winter months tends
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
to have high cloud cover. Therefore, filtered the data to ensure we could collect the best data for each patch based satellite data acquired between the summer months of June and September in the region.
Were any ethical review processes conducted (e-g., by an institutional review board)? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.
N/A
E.4. Prepocessingcckaning/lbbeing
Was any prepocesingddeennngaabliigg of the data done (e-g,, discretization or bucketing,tokenization,, part-of- speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description.
In the Dataset section of our submission, we provide detailed description of the quality control, cleaning, and labeling processes applied to the data obtained from the Alberta Energy Regulator, which forms the basis of our dataset. The satellite imagery utilized in this project was acquired under the Education & Research license from Planet Labs. We reprojected the raw data to EPSG:3261 I using the nearest resampling method. Additionally, we removed all geographic metadata, such as image bounds and coordinates, from the shared data imagery to ensure compliance.
Was the "raw" data saved in addition to the preprocesseddceeneee/abbeeee data (e.g., support unanticipated future uses)? If so, please provide link or other access point to the "raw" data.
The raw satellite imagery data has been saved for internal use; however, it cannot be shared in its current form. Before sharing, the data must undergo preprocessing to remove metadata, as stipulated by the agreement mentioned earlier.
Is the software that was used to preprocess/clean/lbbel the data available? so, please provide a link or other access point.
We plan share the relevant code used for dataset quality control, patch creation, dataset splitting, data acquisition, and label and HDF5 file creation with the public release of the dataset in the future.
Any other comments? N/A
E.5. Uses
Has the dataset been used for any tasks already? If so, please provide a description. Currently, there are no public demonstrations of the AWD Dataset in use. In this work, we showcase its application for Binary Segmentation and Binary Object Detection of Well Sites to train algorithms for accurately locating well sites. These algorithms can be scaled across larger regions of interest to compare against existing databases, identifying potentially undocumented wells. Flagging wells not present in databases crucial, as these could be abandoned wells that are significant emitters of greenhouse gases, making them candidates for plugging.
Is there a repository that links to any or all papers or systems that use the dataset? If so. please provide link or other access point.
N/A
What (other) tasks could the dataset be used for? Additionally, we provide multi-class labels indicating the operational state of the wells for both cases. These labels can be utilized in future projects for locating wells and classifying their operational status, which will aid in identifying well sites that are not present in government records.
Is there anything about the composition of the dataset or the way it was collected and preprocessed/ctened//aa beled that might impact future uses?
This dataset focuses on Alberta, Canada, known for its diverse oil reserves and varied landscapes, providing a represen- tative sample comparable to regions in the Appalachian and Mountain West areas of the United States and some former Soviet states with oil wells and unidentified site issues. A limitation of our study is the reliance on well locations from the Alberta Energy Regulator, which may miss some sites, leading to potential false negatives in the ground data. However, this should have minimal impact on algorithm training, as these labels are a minor part of the dataset, and
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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
deep learning algorithms can handle moderate label noise well (see e.g., (Rolnick et al., 2017)). The main effect may be underreported test accuracy, with some correctly predicted well locations wrongly counted false. We plan to investigate this further in future work. Additionally, the use of multi-spectral optical data in the AWD dataset may limit the models' applicability in regions with frequent cloud cover.
Are there tasks for which the dataset should not be used? If so, please provide description. This dataset is intended for non-commercial use only and should not be utilized in any application that could negatively impact biodiversity.
Any other comments? N/A
E.6. Distribution
Will the dataset be distributed to third parties outside of the entity (e.g, company, institution, organization) on behalf of which the dataset was created? so, please provide a description.
Yes, the dataset will be made public (open-source) in the future.
How will the dataset will be distributed (e.g., tarball on website, API, GitHub)? Does the dataset have digital object identifier (DOI)?
The dataset accessible through Zenodo.
When will the dataset be distributed?
The Dataset is made public and hosted on Zenodo at htttts: http:////eenoooooorrrrrrroooooie
Will the dataset be distributed under copyright other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. The AWD Dataset is released under a Creative Commons Attrbation-NonCCommrriall 4.0 International (CC BY-NC wtt.ooooooooooooooocceeeeeeeeeee
4.0) License (https: : / creat vecommons. org http:/lccneess//ee /tc.c/////////-///..0..0
Have any third parties imposed IP-based other restrictions the data associated with the instances? If so, please describe these restrictions and provide link or other access point to, or otherwise reproduce, any relevant licensing terms, as well as any fees associated with these restrictions.
The satellite imagery used in this project was acquired under the Education & Research license of Planet Labs (PBC, 2024). This license allows for the use of the data in publications and the creation of derivative products, which can be shared association with publications. However, raw imagery cannot be shared publicly. To comply with these guidelines, share the data in HDF5 format, with satellite imagery represented as numpy array from Raster Vector. We have removed all geographic metadata, such image bounds and coordinates, from the shared data. The data is intended for academic use only and should not be used for commercial purposes. Proper credit must be given to the current authors, Planet Labs, and the Alberta Energy Regulator when using this data.
Do any export controls other regulatory restrictions apply to the dataset to individual instances? If so, please describe these restrictions, and provide link other access point to, or otherwise reproduce, any supporting documentation. No
Any other comments? N/A
E.7. Maintenance
Who is supprringhhotingnmattaaingg the dataset?
The dataset hosted on Zenodo.
How can the owner/curator//maagge of the dataset be contacted (e.g., email address)?
You can reach the authors through the email addresses provided in the paper. Additionally, you can raise any issues on the GitHub repository, which will be made public in the future.
27

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Alberta Wells Dataset: Pinpointing Oil and Gas Wells from Satellite Imagery
Is there erratum? If so, please provide link other access point.
Not the best of our knowledge.
Will the dataset be updated (e.g,, to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated users (e-g., mailing list, GitHub)?
As dataset is based data from fixed timeframe and consists of satellite imagery collected during specific period, do not currently have plans to update it in the future. However, if there any changes to these plans, updates to the dataset will be posted on the corresponding GitHub repository once it is made public.
Will older versions of the dataset continue be supporeehhstellmaantaidde??? so, please describe how. If not, please describe how its obsolescence will be communicated to users.
If there are newer versions of the dataset, they will maintain the same format. We will ensure that the code associated with the project on GitHub supports these updates, and we will update the READMEs to reflect any changes to the dataset.
If others want extend/augment/huidd on/contribute the dataset, is there mechanism for them do so? so, please provide description. Will these contributions be validated/verifidd? If so, please describe how. If not, why not? Is there process for commumicating/ddstrrbttti these contributions to users? If please provide a description. We plan to share the relevant code in the future. However, to ensure the ability to compare against our results, we encourage those who wish to build on the dataset to publish their work separately rather than adding to our data repository.
Any other comments? N/A
28


Reviews:
[{"review_id": "asFQsaQuef", "reviewer": "Reviewer_AWji", "summary": "This work proposes a large-scale remote sensing multispectral dataset for pinpointing oil and gas wells. The data comes from real scenes, and the authors carefully designed a reasonable data filtering method and data split scheme to ensure the quality of the data. This work proposes binary segmentation and object detection tasks for oil and gas well pinpointing, and uses a variety of mainstream models in the field of deep learning for training to obtain a benchmark. This large-scale benchmark has made a significant contribution to the field of climate change mitigation.\n\n## update after rebuttal\n\nThanks for the rebuttal, which has addressed most of my concerns. I would like to maintain my original rating.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful and constructive review. We're grateful for your recognition of the dataset's potential impact on climate change mitigation, as well as for your helpful suggestions. Below are our responses to the points you raised:\n\n## Transformer-based models for well detection\n\nThank you for bringing this up. In our initial detection benchmarks, we focused on lightweight models (like SSD Lite and FCOS) because of their practical deployment relevance. However, we agree it is valuable to include transformer-based detection models for a more comprehensive comparison. We have performed experiments with the transformer-based model DETR, using a ResNet50 backbone. As shown below, DETR achieves strong localization performance, further validating the relevance of transformer-based models for this task.\n\nTable R1 : Object detection results on the test set. We report Intersection over Union (IoU) at thresholds 0.1, 0.3, and 0.5, as well as mean Average Precision (mAP) at IoU = 0.5 and IoU ∈ [0.5, 0.95].\n| Architecture | Backbone    | IoU_0.1          | IoU_0.3          | IoU_0.5          | mAP_50           | mAP_50:95        |\n|--------------|-------------|------------------|------------------|------------------|------------------|------------------|\n| FCOS         | ResNet50    | 34.79 ± 0.99     | 48.51 ± 0.59     | 62.66 ± 0.43     | 9.67 ± 1.47      | 30.46 ± 3.11     |\n| DETR         | ResNet50    | **41.78 ± 0.11** | **51.15 ± 0.14** | **63.17 ± 0.11** | **15.22 ± 0.28** | **38.45 ± 0.31** |\n\nWe also appreciate the reviewer’s point regarding the statement on Page 7, Lines 361–363 comparing object detection and segmentation performance. While earlier detection models such as RetinaNet and Faster R-CNN underperformed compared to segmentation counterparts, our updated DETR results demonstrate that transformer-based detectors can help close this gap. We will revise the manuscript to present this comparison more carefully and to avoid overgeneralizing across architectures.\n\n## Clustering algorithm\nThe goal of our clustering algorithm is to allow for a dataset split that avoids spatial autocorrelation across splits while still including many different locations in each split. By construction, our clusters demonstrate spatial coherence, and as shown in Figure 2 they are geographically distributed. (Note that since the wells are unevenly distributed across Alberta, a simple gridding approach would lead to significant imbalances between splits.) While we would be interested in analyzing our algorithm’s efficacy across similar geospatial datasets, we feel this would distract from the principal focus of the present paper and is not necessary to establish that the dataset splits are reasonable.\n\n## Annotation granularity and quality control\n\nThank you for raising this important point. We use standardized 90m circular masks and square bounding boxes for several reasons. Firstly, well pads are relatively standardized in size and shape; the 90m circular shape is typical, not merely a placeholder. Secondly, the very large size of our dataset would make the creation of human-annotated masks challenging, especially given the expert knowledge required for annotation in many cases. Finally, the imagery we use includes a near-infrared channel (and certain well pads are not visible in RGB alone), which makes human interpretation of the images harder. \n\nDespite the coarse annotations, our results show that modern models achieve strong performance. This aligns with prior work (e.g., Rolnick et al., 2017) showing that deep learning models are robust to modest label noise. We will be exploring partial human annotations in future versions of the dataset, in collaboration with domain experts.\n\n## Figure readability\nThank you for the suggestion. We will revise Figure 3 and other figures by reorganizing subpanels, standardizing annotations, and increasing resolution to better highlight qualitative comparisons between the ground truth and predictions.\n\n## Use of metadata for multimodal extensions\nThank you for the suggestion. However, due to licensing, we can release imagery but not metadata. We’re actively exploring similar tasks using open data sources (e.g., Sentinel) to support future multimodal benchmarks.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.\n\nThe amount of benchmark data proposed in this work is much larger than that of previous work, and has wider geographical area characteristics and gas well characteristics, which means the data distribution is richer.", "theoretical_claims": "The 2-step clustering algorithm for dataset split.\n\nThis clustering method is based on the consistency between the distribution of oil and gas wells and geographical features. I hope the author can provide further research to prove the effectiveness of this clustering algorithm, or provide a basis for the method.", "experimental_designs_or_analyses": "Binary detection: \n\nWhy didn't the authors add the Transformer-based model to the binary detection task for comparison as you did in the segmentation experiment? Therefore, the statement \"performance in the object detection task is overall lower than for segmentation\" in the analysis of the detection results on Page 7, Line 361-363 is not completely rigorous and reliable.", "relation_to_broader_scientific_literature": "Previous works have used Google Earth and Sentinel-2 satellite images to construct remote sensing image datasets for oil and gas well detection. However, these datasets are small in scale, cover a small geographical area, and lack extensive data distribution. This work makes efforts to bridge the gap.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "Strengths: \n\n- The paper is well organized and easy to read. The proposed benchmark is targeted at the needs of the field of climate change and makes important contributions to the society.\n- The dataset fully considers the diversity of data distribution in the real-world scenario, and the proposed dataset has rich distribution in both geographical features and oil well status.\n\nWeaknesses:\n\n- The labeling of the dataset is too rough. The oil and gas wells are directly labeled with a fixed size, which lacks quality control and is not conducive to the training of deep learning models.\n- The images in the paper need to be optimized. For example, the layout of Figure 3 is messy and difficult to read.", "other_comments_or_suggestions": "If the license allows, I suggest further utilizing the rich gas well information in the metadata when constructing the data, and proposing multimodal oil and gas well pinpointing tasks, such as referring segmentation, which may further contribute to the community.", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "None"}}, {"review_id": "HvupF3OQSY", "reviewer": "Reviewer_8hHT", "summary": "This paper introduces the Alberta Wells Dataset, the first large-scale benchmark dataset for detecting oil and gas wells from satellite imagery. The dataset contains over 213,000 wells (abandoned, suspended, and active) across Alberta, Canada, represented in high-resolution (3m/pixel) multi-spectral satellite imagery from Planet Labs.\n\nThe authors frame well detection as both binary segmentation and object detection tasks, providing appropriate annotations for each approach. They evaluate several deep learning architectures as baselines, including U-Net, UperNet, FCOS, and SSD Lite. Their experiments demonstrate that including near-infrared spectral bands significantly improves detection performance compared to RGB-only data, and that training on all well types outperforms training on active wells alone.\n\nThe paper introduces a novel dataset splitting algorithm that ensures geographical diversity across training, validation, and test sets. Quality control was performed with domain experts to refine the regulatory data from the Alberta Energy Regulator.\n\nThe dataset addresses a significant environmental challenge - abandoned wells that leak methane into the atmosphere and toxic compounds into groundwater - by providing a resource for developing algorithms to detect wells that may not appear in official records.\n\n## Update after rebuttal\nThank you for your thoughtful rebuttal. I maintain my original recommendation of a weak accept. The Alberta Wells Dataset itself represents a valuable contribution to the field, particularly in addressing an important environmental challenge through the collection and annotation of high-resolution multi-spectral imagery. The dataset's creation and quality control represent significant work that will benefit the research community, which justifies acceptance despite the experimental limitations.\nThe additional DETR results showing strong localization performance are encouraging, and I appreciate the authors' commitment to include failure case analysis in the camera-ready version. While I still believe that multi-class modeling and transfer learning to publicly available imagery would greatly strengthen the practical impact, these could be addressed in future work as the authors suggest. The core contribution of the dataset itself remains valuable and worthy of publication.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your extremely thorough and thoughtful review. We very much appreciate the helpful and constructive feedback. We respond to specific comments and questions below:\n\n## Architecture comparisons and hyperparameter tuning:\n\nThank you for raising this point. We agree that fully tuning hyperparameters in all experiments could be valuable, but believe our use of standard performant settings during training nonetheless provides meaningful benchmarks, especially given that we did not observe significant sensitivity in our models. \n\nWe have obtained additional experimental results on the object detection task for the transformer-based model DETR, using a ResNet50 backbone. As shown below, DETR achieves strong localization performance, particularly at higher IoU thresholds and mAP. \n\nDespite these performance gains, our more lightweight models (e.g. SSD Lite and FCOS) may be more usable in practice, especially for remote sensing practitioners outside of ML.\n\nTable R1 : Object detection results on the test set. We report Intersection over Union (IoU) at thresholds 0.1, 0.3, and 0.5, as well as mean Average Precision (mAP) at IoU = 0.5 and IoU ∈ [0.5, 0.95].\n| Architecture | Backbone    | IoU_0.1          | IoU_0.3          | IoU_0.5          | mAP_50           | mAP_50:95        |\n|--------------|-------------|------------------|------------------|------------------|------------------|------------------|\n| FCOS         | ResNet50    | 34.79 ± 0.99     | 48.51 ± 0.59     | 62.66 ± 0.43     | 9.67 ± 1.47      | 30.46 ± 3.11     |\n| DETR         | ResNet50    | **41.78 ± 0.11** | **51.15 ± 0.14** | **63.17 ± 0.11** | **15.22 ± 0.28** | **38.45 ± 0.31** |\n\n## Multiclass vs binary problem\n\nThank you for bringing up this point. We agree that there are multiple different use cases (and relevant stakeholders) associated with detection of abandoned vs active wells. However, binary detection is nonetheless very useful, as (i) determination of well types can often be done by hand, as detection is the more time-intensive step, and (ii) where centralized records for wells exist, active wells are more likely to be documented than abandoned wells, meaning that in many cases newly discovered wells are likely to be abandoned. There is also considerable noise in well status labels, and the visual distinctions between the different classes are blurry (e.g. a recently abandoned well may be difficult to distinguish from a suspended or active well).\n\n## Data augmentation\nWe agree that further data augmentation experiments could be helpful. We will aim to evaluate several other simple techniques in the camera-ready version, in addition to resizing and flipping. \n\n## Accessibility of Planet data\n\nThank you for raising this excellent point. We agree that for certain users and application settings, Planet data will be less accessible than Landsat / Sentinel imagery. However, a large number of research institutions already have subscriptions to Planet imagery. Furthermore, for many use cases, a user may wish to target a relatively narrow area (e.g. abandoned wells within a regional jurisdiction that can be targeted for plugging). For some stakeholders, it also seems likely that applying algorithms to very large areas will be limited by computational constraints (given the relatively high resolution of the images), independent of data access.\n\nWe very much appreciate your suggestion to use Planet data to train teacher models, then derive student models for lower resolution publicly available data. This sounds like a fruitful follow-up paper, which we would be happy to mention in the conclusion.\n\n## Failure analysis:\n\nThank you for raising this point. In the camera-ready version, we will include representative failure cases from visually complex scenarios. Most patches contain only 1–5 wells, and we observe performance degradation in rare, high-density regions. Abandoned and suspended wells—due to their subtle visual signatures and lack of surface infrastructure—can be especially challenging to detect. Enhancing model performance in these edge cases is a priority for future work.", "claims_and_evidence": "The key claims in the paper are adequately supported by evidence:\nThe dataset's scale (213,000+ wells) and composition are well-documented with detailed statistics. The performance benefits of multi-spectral imagery over RGB-only data are clearly demonstrated in Table 6, showing improved IoU and F1 scores. Similarly, Table 7 provides convincing evidence that training on all well types outperforms training on active wells alone.\n\nHowever, several claims lack sufficient supporting evidence:\n- The claim about Alberta's geographical diversity being sufficient for generalization is not validated with any cross-region testing.\n- The comparative performance of different architectures is inconclusive since hyperparameter tuning is not covered.\n- The authors don't provide evidence about the detection performance specifically for abandoned wells.", "methods_and_evaluation_criteria": "Appropriate aspects:\n- Using both segmentation and object detection approaches makes sense given the nature of the task.\n- Standard evaluation metrics (IoU, F1-score, mAP) are appropriate for these computer vision tasks\n- The dataset splitting algorithm ensuring geographical diversity is well-designed\n- Comparing RGB vs. RGB+NIR performance directly addresses the value of multi-spectral data\n\nLimitations:\n- The lack of hyperparameter optimization makes the architecture comparisons inconclusive.\n- Limited data augmentation techniques (only resizing and basic flipping) don't address the full range of appearance variations.\n- No evaluation of performance specifically on abandoned wells, despite their environmental significance.\n- Absence of error analysis or visualization of failure cases to understand model limitations", "theoretical_claims": "The paper does not make formal theoretical claims requiring mathematical proofs. This is primarily an empirical contribution focused on dataset creation and baseline benchmarking rather than theoretical advancement.", "experimental_designs_or_analyses": "The experiments are generally sound. I list the following Issues:\n- Multi-class analysis absence: Despite creating multi-class annotations (active/suspended/abandoned), the authors only performed binary detection, limiting insights about performance on environmentally critical abandoned wells specifically.\n- Limited data augmentation: Only basic resizing and flipping were used, neglecting more sophisticated augmentations.\n- No transfer learning evaluation: The experiments don't assess if models trained on high-resolution commercial imagery can generalize to publicly available imagery, limiting practical applicability.\n- Missing error analysis: No breakdown of performance by well type or geographical context is provided, offering limited understanding of where models succeed or fail.", "relation_to_broader_scientific_literature": "Oil and gas infrastructure detection datasets: \n- The paper advances prior work by creating a dataset orders of magnitude larger than existing ones. Previous datasets like NEPU (Wang et al., 2021) contained just 1,192 wells, and even larger collections like the Well Pad Dataset (Ramachandran et al., 2024) only included 12,490 wells, compared to this paper's 213,447 wells.\n- Benchmark datasets in remote sensing: The paper follows the model of other domain-specific remote sensing benchmarks like BigEarthNet for land use classification (Sumbul et al., 2019) and CropHarvest for agriculture (Tseng et al., 2021).\n- Methane emissions detection: This work complements recent efforts to identify methane sources, such as the METER-ML dataset (Zhu et al., 2022), by focusing on the infrastructure that may be emitting methane.\n- Computer vision for climate action: The work extends the growing body of research applying computer vision to climate challenges, particularly those focused on monitoring fossil fuel infrastructure.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "The biggest contribution of the paper is the acquisition & processing of (commercial) Planet Labs imagery and pairing it with quality-controlled labels. Details:\n- Access to premium data: Planet Labs' high-resolution (3m/pixel) multi-spectral imagery is commercial and not freely available like Landsat or Sentinel.\n- Weak supervision at scale: The authors essentially created a weak supervision pipeline by combining the Alberta Energy Regulator's records with the satellite imagery, then having domain experts refine and validate the dataset.\n- Processing and standardization: They've done the heavy lifting of processing commercial imagery (standardizing well site diameter annotations at 90 meters, managing cloud cover, ensuring temporal alignment, etc.) which saves other researchers substantial effort.\n\nThe limitations of the paper:\n- Problem framing: avoiding multi-class modeling conflicts with the different purposes of such data. Abandoned Wells Detection's Value is in Immediate regulatory action, Environmental emergency management, and Public health protection. While Active Wells Detection Value is in Transparency and public awareness, Energy production monitoring, and Regulatory compliance verification.\n- Modeling: notable misses are: class balancing, consistency regularization, experimenting with more data augmentation techniques (like crop-zoom, noising, color jitter, rotation, etc), and hyper-parameter tuning for each architecture.\n- Evaluation: lack of breakdown by well type & error analysis (failure modes).\n- Impact: Environmental agencies and researchers working across multiple regions can't realistically purchase Planet imagery for large-scale monitoring, creating models that only work on premium data means the approach can't be maintained long-term without significant ongoing funding. Without demonstrating performance on public imagery (Landsat, Sentinel, etc.), there's no clear path from their research to practical deployment.", "other_comments_or_suggestions": "Suggestions:\n1. Use high-resolution Planet data to train teacher multi-class segmentation models.\n2. Generate pseudo-labels on the full dataset.\n3. Train student models that work with freely available data (e.g., Sentinel-1/2).\n4. Evaluate the performance trade-offs and scale predictions.", "questions_for_authors": "None", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SFfxH1VXrE", "reviewer": "Reviewer_NcK5", "summary": "The paper presents the large-scale benchmark dataset Alberta Wells for pinpointing oil and gas well, comprising over 210,000 wells and including three classes (abandoned, suspended and active), which frames the problem of identification of wells as a challenge for object detection and binary segmentation. To create a well-distributed dataset, the paper introduces a splitting algorithm based on clustering, which makes the dataset maintain an equal distribution of well and non-well images and be more diverse for evaluation and testing. In experiment, the paper selected the well-known baseline models for binary segmentation and object detection to evaluate their performance on Alberta Wells Dataset, which build up a new benchmark and validate the value of NIR imagery and multiple well types.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for the thoughtful and constructive review. We appreciate your recognition of the dataset’s scale, quality, and importance for climate-relevant applications, as well as your positive assessment of our methodological rigor and experimental design. \n\n\nBelow, we address your questions in further detail. Please let us know if there are other points you would like us to clarify.\n\n\n\n## Generalization beyond Alberta\nThank you for raising this point. While we do not claim that models trained on Alberta data will generalize zero-shot to other geographies, we believe Alberta provides a valuable and representative testbed for developing and benchmarking detection models. It is also an especially impactful region in its own right, given its position as one of the largest oil-producing regions globally. That said, we fully agree on the importance of broader applicability, and future iterations of the benchmark will include other regions to test transfer learning, as you suggest. \n\n## Impact of AER Record Incompleteness / False Negatives\nThank you for raising this important point. Like many regulatory sources, the Alberta Energy Regulator (AER) dataset is known to have occasional omissions—particularly for older, undocumented, or improperly decommissioned wells. While our dataset reflects the most complete and authoritative ground truth publicly available (from the AER ST37 database), we acknowledge the presence of potential false negatives stemming from unrecorded sites.  \n\nThat said, one of our core motivations is to support exactly this gap: enabling ML models trained on known wells to help identify undocumented or potentially missing ones. In that sense, false negatives in the label set may represent real-world deployment opportunities for models rather than just noise in the training data. We are currently exploring whether it is possible to examine by hand a subset of “false” well detections from our models to see whether these in fact represent omissions in the dataset. We can endeavor to have this ready for a camera-ready version. \n\n## Precision Drop in U-Net (Table 7)\nThank you for noticing this. The drop in precision from 0.998 to 0.913 when training on all well types (compared to only active wells) is due to a tradeoff between generality and specificity. Including all three types (active, suspended, abandoned) broadens the model’s learning target, introducing more subtle and ambiguous visual patterns—especially for older, overgrown, or decommissioned sites. This leads to slightly more false positives in some regions, but substantially improves recall, F1-score, and class-wise generalization, as demonstrated in our class-specific breakdown .", "claims_and_evidence": "The authors present a comprehensive procedure of data collection, dataset splitting and label creation. Besides the detailed description of the experiments and quantitative results for both segmentation and detection tasks make the claims evidential.", "methods_and_evaluation_criteria": "The authors select a series of well-known baseline models for both segmentation and detection tasks, and they provide a thorough evaluation using standard metrics such as IoU, mAP, precision, recall, and F1-score. The dataset splitting algorithm, which is explained in detail in the form of pseudo-code, ensures a diverse and representative distribution of wells across different geographical region. The value provided by the inclusion of NIR imagery and two new types of wells is proved through comparative experiment.", "theoretical_claims": "The paper does not make too many theoretical claims but rather focuses on the practical application of deep learning models to a real-world problem and a new dataset. The theoretical foundation of the models used are well-established in the literature, and the paper does not attempt to extend these theories. Instead, it focuses on the evaluation of these models on a novel dataset.", "experimental_designs_or_analyses": "This paper tests a series of well-known models for semantic segmentation and target detection on Alberta Wells Dataset and analyzes the performance differences in terms of their framework features to set a benchmark. The evaluation of models is comprehensive, with respect to IoU, Precision, Recall, and F1-Score. The authors also consider the value provided by inclusion of NIR imagery and all three types of well. However, the paper doesn’t provide a more detailed discussion of the potential impact of label noise on the results.", "relation_to_broader_scientific_literature": "The paper sits well within the broader remote sensing and machine learning for environmental monitoring literature. The authors reference previous work on oil and gas infrastructure detection and they highlight the novelty of their dataset in terms of its scale and focus on abandoned wells, which contributes to the work on using remote sensing and machine learning for climate change mitigation.", "essential_references_not_discussed": "The references in this article are adequate and specific. It fully describes the shortcomings of existing datasets in the detection of oil and gas infrastructure, and introduces the role that existing models can play on remote sensing imagery.", "other_strengths_and_weaknesses": "Strengths:\n•\tThe introduction of a large-scale, high-quality dataset for an impactful problem of climate change and greenhouse gas emissions.\n•\tThorough evaluation of well-known models, with clear results and insights, to set a benchmark.\n•\tInclusion of NIR imagery and comparison of models trained on different well types to validate the value of the dataset.\nWeaknesses:\n•\tLimited discussion of the potential impact of label noise on the results.\n•\tThe dataset is limited to Alberta, which may limit its generalizability to other regions.", "other_comments_or_suggestions": "The paper is well-written and provides a valuable contribution to the field. However, adding some experiments in transfer learning based on this dataset to other regions will be better to validate the generalizability.", "questions_for_authors": "1.\tI wonder if it is possible to address the influence false negatives in the dataset due to missing well locations in the Alberta Energy Regulator's data.\n2.\tIn Table 7, why does U-Net get a remarkable drop in Precision from 0.998 to 0.913 trained on all three types in the dataset?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: After the discussion phase all reviewers recommended acceptance, noting that the paper is well written and that the proposed dataset/benchmark is a valuable contribution. As a result, the AC decided to accept the paper. Please take the reviewer feedback into account when preparing the camera-ready version.
Decision: Accept (poster)