Drilling Detection with Machine Learning Part 3: Making and mapping predictions

SkyTruth Technical Program Director Ry Covington, PhD explains challenges to generating meaningful predictions from the machine learning model, and outlines solutions.

[This is the final post in a 3-part blog series describing SkyTruth’s effort to automate the detection of oil and gas well pads around the world using machine learning. We hope that this series – and the resources we’ve provided throughout – will help other developers and scientists working on conservation issues to learn from our experience and build their own exciting tools.  You can read the two previous posts in the series here and here.]

SkyTruth Intern Sasha Bylsma and Geospatial Analyst Brendan Jarrell explained how we create training data and implement a machine learning model to detect oil and gas well pads. So, now that we’ve got a trained model, we just need to run it on a few satellite images and put the predictions on a map.  Seems easy enough…  

We started with some Sentinel-2 imagery collected over the Neuquén Basin in central Argentina. This is one of the most heavily drilled areas in Argentina, and we’ve used the Google Earth Engine platform to export a few Sentinel-2 images that we could work with.  

Screenshot of the Neuquén basin in Argentina. 

The images come out of Earth Engine as GeoTIFFs – a pretty standard file format for overhead imagery. We’ve used some Earth Engine magic to reduce the file size of each image so that they’re easier to handle, but they’re still a bit big for the model. The model expects simple, small patches of images: 256 pixels high, 256 pixels wide, and three channels (e.g., Red, Green, Blue) deep. Our Sentinel-2 GeoTIFFs are about 11,000 pixels high by 11,000 pixels wide, so that left us with a few things to figure out:

  • First, the model is expecting small, simple patches of images – no frills, just pixel values. That means that we have to take the geographic information that’s imbedded in the original GeoTIFF and set aside.  So, how do we do that? 
  • Second, how can we evenly slice up the full image into the small patches that the model is expecting?
  • Third, for every small image patch that the model sees, it returns a small, single channel prediction image with values between zero and one. The closer a pixel is to one, the more likely it is that pixel belongs to a drilling site.  But once the model makes predictions on all of the small images, how do we put them back together in the right order to get the original image’s size and shape?
  • Lastly, how do we take the prediction image and convert it into polygons that we can overlay on a map?

These were all things that we’d never done before, so it’s taken us quite a bit of trial and error to figure out how to make things work. In fact, we’re still working on them – we’ve got a workflow in place, but we’re always trying to refine and improve it. For now, let’s just look at what we’ve got working. 

Step 1. Converting our GeoTIFF to a NumPy array

We started off with a pretty small subset of a Sentinel-2 image that we could experiment with. It’s 1,634 pixels high, 1,932 pixels wide, and 3 channels deep. In developer’s parlance, its shape is: (1634, 1932, 3). The image is of Mari Menuco Lake in Argentina. There are a few dozen drilling sites along the southern portion of the lake that seemed like an ideal place to test out our workflow. Once we had everything working like expected, we’d run the larger Sentinel-2 images through.  

First, we used the GDAL Python API to load our image and collect its (a) geotransform and (b) projection. So, what are these two things? Well, basically, the geotransform is the formula that GDAL uses to go from pixel space (think rows and columns) to geographic space (think x [longitude] and y [latitude]), and the projection is just the coordinate reference system of the image. After we had those two pieces of information set aside for later, we pushed all of the image bands into an NumPy array. 


# Get geographic information.
projection = tiff.GetProjection()           

# Set spatial reference.
spatial_ref = osr.SpatialReference()     # Create empty spatial reference.
spatial_ref.ImportFromWkt(projection)    # Read the "projection" string.

# Collect all the bands in the .tif image.
bands = [tiff.GetRasterBand(band+1) for band in range(tiff.RasterCount)]

# Read each band as an array.
arrays = [band.ReadAsArray() for band in bands] 

# Combine into a single image. 
image = np.array(arrays)

# Format as (height, width, channels).
image = image.transpose(1,2,0)

GDAL reads and writes images differently than NumPy, so the last thing we did was transpose the axes to put our image in the format that we needed: height, width, channels.   

Step 2. Slicing our NumPy array and running predictions 

The next bit was tricky for us to figure out. We needed to take our image – 1634 by 1932 by 3 (1634, 1932, 3) – and slice it into even squares of (256, 256, 3). Our first problem: neither 1634 nor 1932 divides by 256 evenly, so we needed to figure out how to make the image patches overlap just enough to get a clean division.  

Our second problem: we also needed to keep track of where each patch lived in the larger image so that we could put the predictions back together in the right order later. We ended up giving each patch an ID and collecting the coordinates of their top-left pixel (their minimum x and minimum y). We pushed that information into a pandas dataframe – basically, a 2-D matrix of rows and columns – that we could set aside to rebuild our prediction image later.  

Many thanks to CosmiQ Works and all of the participants in the SpaceNet Challenges; the code snippets and GitHub repos that they’ve made available were immensely helpful for us as we tried to figure out how to implement this step.


# Set some variables.
patchSize = 256
overlap = 0.2
height, width, bands = image.shape
imgs, positions = [], []
columns = ['xmin', 'ymin']

# Work through the image and bin it up.
for x in range(0, width-1, int(patchSize*(1-overlap))):    
   for y in range(0, height-1, int(patchSize*(1-overlap))):
       # Get top-left pixel.
       xmin = min(x, width-patchSize)
       ymin = min(y, height-patchSize)

       # Get image patch.
       patch = image[ymin:ymin+patchSize, xmin:xmin+patchSize]

       # Set position.
       pos = [xmin, ymin]

       # Add to array.

# Convert to NumPy array.
imageArray = np.array(imgs) / 255.0

# Create position datataframe.
df = pd.DataFrame(positions, columns=columns)
df.index = np.arange(len(positions))

Once we had the new array of patches – 80 patches of 256 by 256 by 3 – it was easy to run the model and generate some predictions.

# And, go. Don't forget the batch dimension.
predictions = model.predict(imageArray, batch_size=20, steps=4)

Step 3. Rebuilding our image

The model returns an array of predictions – (80, 256, 256, 1). The prediction values range from zero to one. So, a pixel value of .82 means that the model is 82% confident that pixel belongs to a drilling site.  

Side by side comparison of an image and its prediction.

We used the pandas dataframe that we made earlier to put all of these predictions back together in the right order and get the original image’s size and shape. The dataframe was where we recorded the ID and top-left pixel (their minimum x and minimum y) of each patch. First, we created an empty image that is the same size and shape as the original. Next, we went through the dataframe, took out each patch, and added it to the new empty image in the right spot (its top-left pixel).  


# Create numpy zeros of appropriate shape.
empty_img = np.zeros((height, width, 1))

# Create another zero array to record where pixels get overlaid.
overlays = np.zeros((height, width, 1))

# Iterate through patches.
for index, item in positions.iterrows():

   # Grab values for each row / patch.
   [xmin, ymin] = item

   # Grab the right patch.
   slice = predictions[index]
   x0, x1 = xmin, xmin + patchSize
   y0, y1 = ymin, ymin + patchSize

   # Add img_slice to empty_img.
   empty_img[y0:y1, x0:x1] += slice

   # Update count of overlapping pixels.
   overlays[y0:y1, x0:x1] += np.ones((patchSize, patchSize, 1))            

# Normalize the image to get our values between 0 and 1. 
rebuilt_img = np.divide(empty_img, overlay_count)

Most of our team are visual thinkers, so the easiest way for us to imagine rebuilding the image is like covering a blank sheet of white paper in pink sticky-notes, and then smoothing them all down to get a new, blank sheet of pink paper.   

Step 4. Converting our predictions to polygons

After rebuilding our prediction array to be the same size and shape as the original satellite image, we used the GDAL Python API to convert it into polygons that could go on a map. To try and clean things up a bit, we started by selecting only those pixels where the model was more than 70% confident they belonged to a drilling site. We set anything under that threshold to zero. This just helped us to eliminate some noise and clean up the predictions a bit. With that done, we used GDAL to convert the cleaned up prediction image into polygons and reattach the spatial information that we set aside at the beginning (i.e., the geotransform and the projection).  


# Band to use.
sourceBand = output.GetRasterBand(1)

# Set up shapefile.
shpDrv = ogr.GetDriverByName("ESRI Shapefile")                                
outFile = shpDrv.CreateDataSource("/gdrive/My Drive/detections.shp")      
layer = outFile.CreateLayer("detections", srs=spatial_ref)                    

# Add field.
idField = ogr.FieldDefn("id", ogr.OFTInteger)               

# And, convert. 
gdal.Polygonize(sourceBand, sourceBand, layer, 0, [], None)

And at this point we had our shapefile. From there, it was easy to upload that shapefile as an asset into our Earth Engine account and have a look at our predictions over satellite imagery. We did a bit of clean up and editing to make sure that all of the polygons look good — what’s sometimes called “human-in-the-loop” modeling — but, for the most part, we were all done.

Screenshot of the polygons in EE.

Lastly, we did a quick assessment to see how well our from-scratch workflow functioned. In the image above, red points are drilling sites that we got correct (true positives), green points are drilling sites that we missed (false negatives), and blue points are places where the model thought there was a drilling site when there wasn’t (false positives). Here are the numbers: 

Total number of validated ground truth points: 239
True Positives: 107
False Positives: 50
False Negatives: 82
Precision: 0.6815286624203821
Recall: 0.5661375661375662
F1-score: 0.6184971098265896

Precision, recall, and F1-score are all just metrics for understanding how a model performs. Your spam folder offers a good example. Imagine that your email’s spam model makes 30 predictions. Precision would measure the percentage of emails flagged as spam that it correctly classified as spam. Recall would measure the percentage of actual spam emails that it correctly classified as spam. Often, these two things are in tension – if the model is more aggressive (i.e., lowers the threshold for an email to be classified as spam) the recall will go up since they’d be capturing more actual spam emails. But the precision would go down, because they’re also capturing more emails overall, and it’s pretty likely that many of those won’t be spam. The F1-score builds off of precision and recall, and it’s probably easiest to think of it as a measure of overall accuracy. In the context of the drilling site work, our precision and recall numbers mean that 68% of the things we’re predicting as drilling sites actually are drilling sites, but we’re only picking up 56% of the drilling sites that are actually out there in the world. 

We hope that this series has helped to demystify the machine learning workflow for others. Figuring out how to build a machine learning data processing pipeline from scratch was an exciting challenge for us. We’re encouraged by our progress so far, but as the metrics above indicate, there’s still lots of room for improvement. We will keep at it because this technology stack — integrating remote sensing with cloud computing and machine learning — is at the heart of our Conservation Vision initiative to automate the analysis of imagery, to solve challenging conservation problems. 

So please stay tuned for updates in the future.  And don’t hesitate to send us a note if you have questions about what we’ve done or ideas about how we can improve things going forward.  We welcome the collaboration! 


Drilling Detection with Machine Learning Part 2: Segmentation Starter Kit

Geospatial Analyst Brendan Jarrell explains, step-by-step, how to develop a machine learning model to detect oil and gas well pads from satellite imagery.

[This is the second post in a 3-part blog series describing SkyTruth’s effort to automate the detection of oil and gas well pads around the world using machine learning. This tool will allow local communities, conservationists, researchers, policymakers and journalists to see for themselves the growth of drilling in the areas they care about. This is a central part of SkyTruth’s work: to share our expertise with others so that anyone can help protect the planet, their communities, and the places they care about. You can read the first post in the series here. All of the code that will be covered in this post can be found here. Our training dataset is also available here.]

SkyTruth Intern Sasha Bylsma explained in our first post in this series how we create training data for a machine learning workflow that will be used to detect oil and gas well pads around the world. In this post, I’m going to explain how we apply a machine learning model to satellite imagery, explaining all the tools we use and steps we take to make this happen, so that anyone can create similar models on their own.

Once we have created a robust set of training data, we want to feed a satellite image into the machine learning model and have the model scan the image in search of well pads. We then look to the model to tell us where the well pads are located and give us the predicted boundary of each of the well pads. This is known as segmentation, as shown in Figure 1. 

Figure 1: An example of our current work on well pad segmentation. The original image is seen on the left; what the ML model predicts as a well pad can be seen on the right. Notice that the algorithm is not only returning the drilling site’s location, but also its predicted boundaries.

We want the model to identify well pad locations because of the crucial context that location data provides. For example, location can tell us if there is a high density of drilling in the area, helping nearby communities track increasing threats to their health. It can also calculate the total area of disturbed land in the area of interest, helping researchers, advocates and others determine how severely wildlife habitat or other land characteristics are diminished.  

In the past, SkyTruth did this work manually, with an analyst or volunteer viewing individual images to search for well pads and laboriously drawing their boundaries. Projects like FrackFinder, for example, may have taken staff and volunteers weeks to complete. Now, with the help of a machine learning model, we can come in on a Monday morning, let the model do its thing, and have that same dataset compiled and placed on a map in an hour or two. The benefits of leveraging this capability are obvious: we can scan thousands of images quickly and consistently, increasing the likelihood of finding well pads and areas with high levels of drilling.

Formatting the data

So how do we do this? The first thing we need to do is get our data into a format that will be acceptable for the machine learning model. We decided that we would use the TensorFlow API as our framework for approaching this task. TensorFlow is an open-source (i.e. “free-to-use”) software package that was developed by Google to give users access to a powerful math library specifically designed for machine learning. We exported data from Google Earth Engine in the TFRecord format; TFRecords are convenient packages for exporting information from Earth Engine for later use in TensorFlow. In our code under the section labeled “Get Training, Validation Data ready for UNET,” we see that there are a few steps we must fulfill to extract the TFRecords from their zipped up packages and into a usable format (see Figure 2). 

# Bands included in our input Feature Collection and S2 imagery.

bands = ['R','G','B']
label = 'Label'
featureNames = bands + [label]
# Convert band names into tf.Features.

cols = [
         tf.io.FixedLenFeature(shape=[256,256],dtype=tf.float32) for band in featureNames

"""Pass these new tensors into a dictionary, used to describe pieces of the input dataset."""
featsDict = dict(zip(featureNames,cols))

Figure 2:  Preprocessing code

Second, we create Tensorflow representations of the information we are interested in drawing out of each of our examples from the Google Earth Engine workflow (see the first post in this series for more explanation on how we made these samples). Each of the samples has a Red, Green, and Blue channel associated with it, as well as a mask band, called “label” in our code. As such, we create Tensorflow representations for each of these different channels that data will be plugged into. Think of the representations we create for each channel name as sorting bins; when a TFRecord is unpacked, the corresponding channel values from the record will be placed into the bin that represents it. After loading in all of our TFRecords, we push them into a TFRecord Dataset. A TFRecord Dataset is a dataset which is populated by several TFRecords. We then apply a few functions to the TFRecord Dataset that make the records interpretable by the model later on.

Validation dataset

Once the dataset is loaded in, we split the dataset into two. This is an important part of machine learning, where we set aside a small amount of the whole dataset. When the model is being trained on the larger portion of the dataset, known as the training data, it will not see this smaller subset, which we call the validation set. As its name suggests, the model uses this smaller fraction of information to perform a sanity check of sorts. It’s asking itself, “Okay, I think that a well pad looks like this. Am I close to the mark, or am I way off?” All of this is put in place to help the model learn the minute details and intricacies of the data we’ve provided it. Typically, we will reserve 15-30% of our total dataset for the validation set. The code necessary for splitting the dataset is shown in Figure 3 below.

# Get the full size of the dataset.
full_size = len(list(data))
print(f'Full size of the dataset: {full_size}','\n')

# Define a split for the dataset.
train_pct = 0.8
batch_size = 16
split = int(full_size * train_pct)

# Split it up.
training = data.take(split)
evaluation = data.skip(split)

# Get the data ready for training.
training = training.shuffle(split).batch(batch_size).repeat()
evaluation = evaluation.batch(batch_size)

# Define the steps taken per epoch for both training and evaluation.
TRAIN_STEPS = math.ceil(split / batch_size)
EVAL_STEPS = math.ceil((full_size - split)  / batch_size)

print(f'Number of training steps: {TRAIN_STEPS}')
print(f'Number of evaluation steps: {EVAL_STEPS}')

Figure 3: Validation split code snippet

Implementation in U-Net

Now it’s time for the fun stuff! We’re finally ready to begin setting up the model that we will be using for our segmentation task. We will be leveraging a model called a U-Net for our learning. Our implementation of the U-Net in TensorFlow follows a very similar structure to the one seen in the example here. In a nutshell, here is what’s happening in our U-Net code:

1.) The machine learning model is expecting a 256 pixel by 256 pixel by 3 band input. This is the reason why we exported our image samples in this manner from Earth Engine. Also, by chopping up the images into patches, we reduce the amount of information that needs to be stored in temporary memory at any given point. This allows our code to run without crashing.

2.) The computer scans the input through a set of encoders. An encoder’s job is to learn every little detail of the thing we’re instructing it to learn. So in our case, we want it to learn all of the intricacies that define a well pad in satellite imagery. We want it to learn that well pads are typically squares or rectangles, have well defined edges, and may or may not be in close proximity to other well pads. As the number of encoders increases further down the “U” shape of the model, it is learning and retaining more of these features that make well pads unique.

3.) As the computer creates these pixel-by-pixel classifications sliding down the “U,” it sacrifices the spatial information that the input once held. That is to say, the image no longer appears as a bunch of well pads scattered across a landscape. It appears more so as a big stack of cards. All of the pixels in the original image are now classified with their newly minted predictions (i.e. “I am a well pad” or “I am not a well pad”), but they don’t have any clue where in the world they belong. The task of the upper slope of the “U” is to stitch the spatial information onto the classified predictions generated by our model. In this light, the upward slope of the “U” is made up of filters known as decoders. The cool thing about the U-Net is that as we go further up the “U”, it will grab the spatial pattern associated with the same location on the downward slope of the U-Net. In short, the model gives its best shot at taking these classified predictions and making them back into an image. To see a visual representation of the U-Net model, refer to Figure 4 below.

Figure 4: A graphic representing the U-Net architecture, courtesy of Ronneberger, et al.

At the end of the trip through the model, we are left with an output image from the model. This image is the model’s best guess at whether or not what we’ve fed it shows well pads or not. Of course, the model’s best guess will not be absolute for each and every pixel in the image. Given what it has learned about well pads, (how they’re shaped, what color palette usually describes a well pad, etc.), the model returns values on a spectrum from 0 to 1. Wherever the values land in between these two numbers can be called the model’s confidence in its prediction. So for example, forested areas in the image would ideally show a confidence value near zero; conversely, drilling sites picked up in the image would have confidence values close to one. Ambiguous features in the image, like parking lots or agricultural fields, might have a value somewhere in the middle of zero and one. Depending on how well the model did when compared to the mask associated with the three band input, it will be reprimanded for mistakes or errors it makes using what’s known as a loss function. To read more about loss functions and how they can be used, be sure to check out this helpful blog. Now that we have the model set up, we are ready to gear up for training!

Data augmentation

Before we start to train, we define a function which serves the purpose of tweaking the inputs slightly every time they are seen by the model. This is a process known as data augmentation. The reason why we make these small changes is because we don’t have a large dataset. If we give the model a small dataset without making these tweaks, each time the model sees the image, it will essentially memorize the images as opposed to learning the characteristics of a well pad. It’s a pretty neat trick, because we can make a small dataset seem way larger than it actually is simply by mirroring the image on the y-axis or by rotating the image 90 degrees, for example. Our augmentation workflow is shown in Figure 5.

# Augmentation function to pass to Callback class.
def augment(image, mask):
 rand = np.random.randint(100)
  if rand < 25:
   image = tf.image.flip_left_right(image)
   mask = tf.image.flip_left_right(mask)

 elif rand >= 25 and rand < 50:
   image = tf.image.rot90(image)
   mask = tf.image.rot90(mask)

 elif rand >= 50 and rand < 75:
   image = tf.image.flip_up_down(image)
   mask = tf.image.flip_up_down(mask)


 return (image, mask)

# Callback for data augmentation.
class aug(tf.keras.callbacks.Callback):
 def on_training_batch_begin(self, batch, logs = None):
   batch.map(augment, num_parallel_calls = 5)

Figure 5: Augmentation function and checkpoints cell

Fitting the model to the dataset

Now it’s time to put this model to the test! We do this in a TensorFlow call known as .fit(). As the name suggests, it is going to “fit” the model to our input dataset. Let’s go ahead and take a look at the code from Figure 6, shown below. 

history = UNet.fit(
     x = training,
     epochs = model_epochs,
     steps_per_epoch = TRAIN_STEPS,
     validation_data = evaluation,
     validation_steps = EVAL_STEPS,
     callbacks = [aug(),cp,csv])

Figure 6: Fitting the model to the input dataset

It’s important to conceptually understand what each of the values passed into this function call represents. We start with the variable “x”: this expects us to pass in our training dataset, which was created earlier. The next argument is called epochs. Epochs describe how many times the model will see the entire dataset during the fitting process. This is somewhat of an arbitrary number, as some models can learn the desired information more quickly, thus requiring less training. Conversely, training a model for too long can become redundant or potentially lead to overfitting. Overfitting is when a model learns to memorize the images it’s trained on, but it doesn’t learn to generalize. Think of overfitting like memorizing a review sheet the night before a test; you memorize what is covered in the review, but any minor changes in the way questions are asked on the actual test could trip you up. For this reason, it is generally up to the user to determine how many epochs are deemed necessary based on the application. 

The next argument, steps_per_epoch (also validation_steps) describes how many batches of data should be taken from our training and validation sets respectively through each epoch. Batches are small chunks of the dataset; it is useful to divide up the dataset into batches to make the training process more computationally efficient. One would typically want to go through the whole dataset every epoch, so it’s best to set the steps as such. Validation_data is where we would specify the data we set aside during training to validate our model’s predictions. Remember, that data will not be seen by the model during its training cycle. The last argument is called callbacks. This is where we pass in the augmentation function. This function is instructed by our callback to run at the beginning of each new batch, therefore constantly changing the data during training. We also optionally pass in other callbacks which might be useful for later reference to our training session. Such callbacks might export the loss and metrics to our Google Drive in a comma-separated values format or might save checkpoints throughout the model, keeping track of which training epoch produces the lowest loss. There are many other pre-packaged callbacks which can be used; a full list of these callbacks can be found here. Now that we have all of that covered, it’s time to start learning! By running this code, we begin the training process and will continue until the model has finished running through all of the epochs we specified.

Once that has finished, we save the model and plot its metrics and its loss, as shown in Figure 7. Based upon how these plots look, we can tell how well we did during our training.

Figure 7: An example chart, showing plotted metrics (top) and loss (bottom). Metrics are used to evaluate the performance of our model, while loss is directly used during training to optimize the model. As such, a good model will have a greatly reduced loss by the time we reach the end of training.

And voila! You have made it through the second installment in our series. The next entry will cover post-processing steps of our machine learning workflow. Questions we will answer include:

– How do we make predictions on an image we’ve never seen before?

– How do we take a large image and chop it into smaller, more manageable pieces? 

– How do we take some new predictions and make them into polygons?

Stay tuned for our next entry, brought to you by Dr. Ry Covington, SkyTruth’s Technical Program Director. In case you missed it, be sure to check out the first post in this series. Happy skytruthing!

Correcting Recent Reporting on Offshore Flaring in Guyana

Recent reporting misrepresented SkyTruth data.

We’re always glad to have conservation-minded groups and individuals use our flaring maps, but we would like to correct some errors in how our data was interpreted in two recent articles in the Stabroek News concerning natural gas flaring from an ExxonMobil-owned vessel, the Liza Destiny, anchored off the coast of Guyana. 

In early June, 2020, the Guyana Marine Conservation Society (GMCS) contacted SkyTruth to see if we could help monitor natural-gas flaring from the Liza Destiny. The Liza Destiny had mechanical issues that required it to continuously flare, and GMCS wanted to be able to verify the flaring that ExxonMobil was reporting.

This isn’t a request that SkyTruth can normally help with, but the unique circumstances surrounding the Liza Destiny allowed us to provide GMCS with some meaningful data. Our global flaring map is a visualization of flaring events detected around the world, every day, using satellite data. The source of our data is the Earth Observation Group, which identifies flaring based on measurements of brightness and temperature captured by National Oceanic and Atmospheric Administration satellites. Due to the low level of detail of these images (each pixel represents a spot on the ground about 750 meters across), we usually can’t pinpoint flaring to a specific source such as an individual oil or gas well. However, since there were no other flaring vessels near the Liza Destiny, we could confidently assign all flaring events within the satellite’s accuracy to this vessel. 

In mid-July, GMCS asked for an update containing the most recent data, which we provided by way of this document. The ensuing article in Stabroek News on July 25, 2020, erroneously claimed that our data showed the Liza Destiny was flaring from June 27 through July 7, a period when ExxonMobil reported to the Guyana EPA that there was no flaring because the vessel was undergoing maintenance.

Contrary to what the article suggests, the data SkyTruth provided did not contradict ExxonMobil. Our data did not show flaring on these dates, with the exception of June 28. It’s important to note that the lack of flaring in our data for that time period doesn’t conclusively prove there was no flaring, because clouds can block the satellites’ ability to “see” flares. 

And none of this is to imply that there are not legitimate concerns about the persistent, long-term flaring at this vessel documented in the data we shared with GMCS. 

SkyTruth Visualization and App of Drilling Near Chaco Canyon Available to Activists and Others

The Bureau of Land Management has permitted intensive oil and gas drilling around Chaco Culture National Historical Park, threatening a landscape that supports one of the most important cultural sites in the world.

[This discussion of the threats to Chaco Culture National Historical Park, and SkyTruth tools highlighting that threat, was written as a collaborative effort between SkyTruth team members Matthew Ibarra and Amy Mathews.]

Reminders of an ancient civilization dominate the desert landscape in northwestern New Mexico. Ruins of massive stone “Great Houses,” once several stories high with hundreds of rooms, remain at Chaco Culture National Historical Park. Their complexity and numbers reveal that a sophisticated culture thrived in this place a thousand years ago. Descendants of those native peoples — today’s Pueblo tribes and several Navajo clans — say that Chaco was a central gathering place where people shared ceremonies, traditions, and knowledge. Yet much about Chaco remains a mystery. During the late 1200s, construction of buildings and monuments slowed and the Chacoan people moved from the area. However, Chaco is still considered to be a spiritual and sacred place by many Native Americans. 

Parts of Chaco were first designated as a national monument by President Theodore Roosevelt in 1907. Eighty years later the United Nations recognized the monument as a World Heritage Site because of its unique cultural significance. Despite these protections, the area surrounding the park is now threatened. 

Over the past two decades, the federal Bureau of Land Management (BLM) has allowed oil and gas companies to drill hundreds of wells within 15 miles of the park using the technique known as hydraulic fracturing, or fracking. Fracking typically creates air and noise pollution, threatens water supplies, increases truck traffic on local roads, and harms communities with toxic chemicals. SkyTruth’s data on fracking in Pennsylvania has been used by scientists at Johns Hopkins University to demonstrate some of the harmful health effects associated with fracking. 

Many tribal groups have voiced concerns about the spiritual, cultural, physical and health impacts from drilling in the area. In September of 2019, the U.S. House of Representatives approved the Chaco Cultural Heritage Area Protection Act that would create a 10-mile buffer zone on federal lands around the park to prevent any future leasing for oil and natural gas drilling. Although the entire New Mexico congressional delegation supports this legislation, the Senate has not taken action on this bill. Reportedly, the bill does not have Republican support in the Senate, which substantially reduces its chances of becoming law under the current majority.

To illustrate the extent of drilling in recent years, SkyTruth created an animation of wells surrounding Chaco Culture National Historical Park by illustrating data from New Mexico’s Oil Conservation Division as well as using the most current imagery from  the U.S. Department of Agriculture’s National Agriculture Imagery Program (NAIP) as a backdrop. (See “About the Data” below to learn more about how we did this.) The visualization shows the growth of wells throughout the region surrounding the park, with distances from the park boundary delineated. New wells have emerged throughout the region in this time period, from the park boundary to 15 miles and beyond. The region within 15 miles of the park now contains 33% more oil and gas wells than it did in 2000 — an increase of 367 wells. 

The growth of oil and gas wells within a 15-mile radius of Chaco Culture National Historical Park from 2000 – 2018

Despite local opposition and congressional action, the BLM currently is proposing additional leasing for drilling around the park. The public comment period for input on this leasing plan has been extended to September 25th, 2020. (Click here for information on how to submit comments.) 

In addition to the animation of drilling build out, SkyTruth has also created still images showing the changes around Chaco Canyon from 2000-2018. Each image highlights change in drilling activity for the year and features the most recent NAIP imagery from 2018 as a backdrop.

Still images for each year in the animation

SkyTruth also has developed an interactive app that allows users to view a map of the Chaco Culture National Historical Park and its surrounding area with all the surrounding oil and gas wells. Users will be able to click on a well pad to see more information such as the well pad identification or the status of the well, such as whether it is being plugged, or is still fully operational. (See “About the App” section below.) This app can be viewed here.

Chaco Culture National Historical Park is home to the largest and best preserved ancient architectural structures in all of North America. It was home to communities throughout the 1000s and remains important to Native Americans and others. Today, this magnificent region is becoming an industrialized area cluttered by oil and gas wells and threatens to harm the people who honor this place of heritage. SkyTruth hopes the visualizations and tools we’ve created will help arm activists, draw attention to the leasing process, and support congressional action to protect a remarkable place.

About the Data

The data used to identify wells comes from New Mexico’s Oil Conservation Division. The link for this dataset can be found here, labeled Public FTP Site. This large dataset was analyzed to create buffer zones based on the distance to Chaco Culture National Historical Park. The dataset was used in QGIS — a geographic information system tool — alongside NAIP imagery exported from Google Earth Engine to create an accurate map of the data and wells. We used TimeManager, a plugin for QGIS, to create this visualization. TimeManager allows users to easily add data to the working map based on time. Wells were added to the working map by month starting from January of 2000 through September of 2019, creating over 200 still images. TimeManager also allows users to export these still images as frames to create an MP4 file. We then used Final Cut Pro to add an overlay over this MP4 and create a visualization with a legend, scale bar, and other necessary features. 

About the App  

The Chaco Canyon Well Inspector app allows users to pan and zoom around an interactive map and inspect each individual well around the Chaco Canyon area. Upon clicking an individual well point, data such as the well identification number (API number) and status becomes visible to the user. Users will be able to inspect the area surrounding Chaco Culture National Historical Park to see how the growing number of wells has impacted the surrounding area and gain a better understanding of the status of oil and gas wells in the area.

Update 9/9/20: The animation, photos, and Chaco Canyon Well Inspector app were updated to reflect the spud date at each site; that is, the date when wells officially broke ground for drilling.

AIS Ship Tracking Data Shows False Vessel Tracks Circling Above Point Reyes, Near San Francisco

Analysis from SkyTruth and Global Fishing Watch shows ship tracks jumping thousands of miles from their true locations.

Bjorn Bergman works with SkyTruth and with the Global Fishing Watch research team to track vessels broadcasting false automatic identification system (AIS) locations and to investigate new sources of satellite data for vessel tracking and for detecting dark targets at sea. In this blog post, Bjorn spots an unusual pattern of false AIS broadcasts concentrated at one location, Point Reyes, northwest of San Francisco on the California coast. Why would vessels thousands of miles away be suddenly popping up in circles over Point Reyes? Could this reflect an intentional disruption of the underlying global positioning system (GPS) that AIS relies on, or is there some other explanation for this pattern?

In December 2019, SkyTruth reported on a number of locations on the Chinese coast (mostly oil terminals) where ship tracking positions from the automatic identification system (AIS)  became scrambled as soon as ships approached within a few miles of a point on shore. Importantly, we knew that this was actual disruption of the underlying global positioning system (GPS) — or more broadly the Global Navigation Satellite System — and not just a shipboard AIS malfunction. We determined this because another source of GPS data, Strava’s heat map of fitness trackers, showed the same ring pattern. A quick recent check of the data showed that this GPS manipulation is ongoing at oil terminals in four of the cities (Shanghai, Dalian, Fuzhou, and Quanzhou) where we had detected it last year. We still don’t know if this manipulation is specifically intended to mask ship traffic or if there is some other reason for disrupting GPS.

Following the findings last year on the Chinese coast, I began looking globally for any similar patterns in AIS tracking data around the world. While I haven’t found the precise pattern observed at the Chinese oil terminals outside of China, I did find a somewhat different false AIS broadcast pattern which, strangely enough, appears concentrated above Point Reyes northwest of San Francisco, California in the United States. Although the circling tracks look similar in both locations, the vessels on the Chinese coast were at most a few miles from the circling tracks, while the vessels broadcasting tracks above Point Reyes are actually thousands of miles away. So far I’ve found vessels in nine locations affected. Some of these locations are near oil terminals or where GPS disruption has been reported before, but there is no clear pattern linking all of the affected areas.  

Image 1: AIS tracks from a number of vessels have appeared circling over Point Reyes near San Francisco even though the ships can be confirmed to be thousands of miles away. False circling tracks from five vessels are shown here. AIS data courtesy of Global Fishing Watch / Orbcomm / Spire.

The AIS positions being broadcast over Point Reyes are obviously false (some of them are over land and they show a constant speed and oval pattern we wouldn’t see with a real ship track). But how can we be sure where the ship really is? The most important indication is the location broadcast just prior to the jump to Point Reyes and then where the vessel reappears after the apparent circling finishes. The duration of the circling pattern varies, from less than an hour for one ship in the Indian Ocean, to as much as two weeks for some of the other vessels. However, besides seeing the true locations before and after the jump to Point Reyes, it’s also possible to look at where the AIS receiving satellites were while the vessels were broadcasting positions around Point Reyes.

Image 2: The colored lines show AIS tracks from five of the ships whose broadcast positions jumped suddenly to Point Reyes, California, northwest of San Francisco. The time of the tracking disruption varies from less than one hour for one vessel to about two weeks for some others. Two of the vessels (Princess Janice and Alkahfi Maryam) also have tracks appearing over land in North America. The reason for this displacement is unknown although some of the vessels are in areas where GPS disruption has been reported (Eastern Mediterranean and Sea of Azov). AIS data courtesy of Global Fishing Watch / Orbcomm / Spire.

To get an approximate location for one vessel’s real position during the two weeks it broadcast over Point Reyes and the Western United States, SkyTruth analyst Christian Thomas and I analyzed the footprints of the satellites receiving the AIS positions. This was possible thanks to data Spire Global, Inc. provided to Global Fishing Watch. Spire’s data gives the identity of the receiving satellite with each AIS position. This allowed the Global Fishing Watch research team to access orbit information, which they used to calculate exactly the point above the surface of the earth where each satellite was when it received an AIS position and then calculate the distance from the satellite position to the ship’s broadcast AIS position. Because AIS broadcasts are only received within an approximately 5,000 kilometer (3,100 mile) diameter footprint, we know that the vessel was somewhere within this area. We can even narrow down the location further based on successive passes of AIS receiving satellites. 

Image 3: Broadcast AIS positions from Princess Janice. The track makes multiple jumps between a real location in an oil terminal on the coast of Nigeria (inset lower right) and false positions over the United States. Over two weeks in June 2019 the false track initially circles over Point Reyes northwest of San Francisco before veering over the Pacific and over the interior of the United States. More circling is seen around Salt Lake City Utah (inset upper right). AIS data courtesy of Global Fishing Watch / Orbcomm / Spire.

This vessel, the Princess Janice, is a crew boat traveling to offshore oil installations. It broadcasts a normal track out of a Nigerian oil terminal until June 5, 2019. For the following two weeks the vessel then broadcasts a false location track circling above Point Reyes and eventually veering off above Utah (during this time the track occasionally jumped back briefly to the Nigerian oil terminal). Unlike other false AIS broadcasts we have documented, which have a constant location offset or flipped coordinate values (producing a mirror image of the actual position), these circling tracks appear to not reflect the true movements of the vessel in any way. 

When we looked at the footprint of the satellite receiving AIS positions from Princess Janice, it’s clear that the vessel remained on a stretch of the central Nigerian coast or in nearby waters in the Gulf of Guinea (see Image 4) throughout the two-week period when false locations were being broadcast. 

Image 4: Princess Janice broadcasts an AIS track over Point Reyes near San Francisco and over the Western United States from June 5 – 21, 2019 (see Image 3). Analysis of the footprints for the satellites receiving these positions demonstrates that the vessel was actually within a region on the central Nigerian coast and adjacent Gulf of Guinea. Frame 1: Location over the Earth’s surface (red dots) of satellites receiving false position messages. Frame 2: Extent of satellite footprints for AIS reception (large red circles). Frame 3: Density of satellite coverage overlap, areas of increasing density shown as Blue → Green → Yellow → White. Frame 4: Area where all satellite footprints overlap (maximum coverage) shown in white. The white shaded region on the central Nigerian coast contains the true location of the Princess Janice during the period when the vessel was broadcasting a false location track. Analysis was done in Google Earth Engine using approximate satellite footprints of 5,000 km (3,100 miles) diameter.

Both the manipulated GPS positions seen on the Chinese coast and these new examples over Point Reyes are characterized by rings of positions. The rings have similar shapes, somewhat wider east to west than north to south. However circles appearing over Point Reyes vary greatly in size and the broadcast vessel courses may be oriented clockwise or counterclockwise around the ring. All speeds are exactly 20 knots. In contrast, the rings on the Chinese vessels last year had positions that were 21 or 31 knots with the 31 knot positions always oriented counterclockwise. Critically, while we could confirm that GPS interference caused the rings of AIS positions on the Chinese coast, we don’t yet know if that is the case with the positions over Point Reyes. An alternative is that this is simply a malfunction affecting the individual ships’ AIS systems. We were able to confirm that the false circling positions over Point Reyes occur in data from all available AIS providers (Orbcomm, Spire, and ExactEarth) and in AIS positions received by both satellites and terrestrial receivers.

The list of affected vessels below (Table 1) shows that many types of vessels in different geographic locations have displayed this same pattern of AIS disruption. Some were in areas where GPS problems have been reported by others (the Eastern Mediterranean, Sea of Azov, Libyan coast); other locations are seemingly random. A number of the vessels, but not all, appear near oil terminals and are involved in supporting offshore platforms. 


Table 1: Vessels showing a pattern of false circling AIS positions. Reported locations are where circling tracks appeared (mainly at Point Reyes near San Francisco). Real locations are where the vessel was determined to be while broadcasting the false circling AIS track. AIS data courtesy of Global Fishing Watch / Orbcomm / Spire.

The presence of three of these vessels in areas of documented GPS interference is intriguing. The cargo ship Berezovets shown below was operating in one such area in the Sea of Azov, north of the Black Sea. Following the Russian annexation of Crimea in 2014 and the takeover of Eastern Ukraine by Russian-backed separatists, the front line in the ongoing civil war has cut through Eastern Ukraine north of the Sea of Azov. There have also been conflicts on the water and a Russian blockade of the Kerch Strait leading north from the Black Sea.

Image 5: The Russian flagged cargo ship Berezovets transits through the Sea of Azov in June 2019 and has its AIS track jump suddenly to Point Reyes near San Francisco (inset). Incidents of documented GPS disruption occurred in March 2019 east of the Bilosarai Spit and in July 2019 in the city of Starohnatvka. AIS data courtesy of Global Fishing Watch / Orbcomm / Spire.

The Russian flagged Berezovets transited through the Kerch Strait on June 3, 2019 then headed northeast passing south of the conflict zone towards Russian ports. As the vessel enters Russian waters (location 1 in Image 5) and anchors, its June 4-8 positions broadcast by the AIS system are scrambled, some appearing scattered 20 miles from the vessel’s anchor point. The vessel track then moves east towards port before jumping 20 miles north to a point on land (2) and then jumping about 11,000 miles west to circle above Point Reyes (3). This circling continues for about 60 hours from June 11 – 14, including some irregular positions extending about 40 miles into the Pacific. As with the Princess Janice track, it’s unclear why the false track would jump to California and what accounts for the individual variations in the different tracks we see appearing at this location. On June 14, 2019 the Berezovets AIS track jumps back to the vessel’s real location, now in the Russian port of Azov (4) and can then be seen to proceed eastward up the River Don.  

The unusual disruption in the Berezovets broadcast AIS track was both preceded and followed by similar reported disruptions in GPS in the same region. On March 7, 2019 a Ukrainian military website reported that three vessels on the Sea of Azov experienced failures in their navigation systems. One of these failures occurred the day before, east of Bilosarai Spit (see Image 5). The other two reported disruptions were in the preceding month at other locations in the Sea of Azov. On July 23, 2019 according to a report from the Organization for Security and Co-operation in Europe’s Special Monitoring Mission to Ukraine a UAV (unmanned aerial vehicle) flying over the city of Starohnativka in Ukraine, was one of several UAVs that experienced GPS interference assessed to be likely from jamming. While not conclusive, the proximity of these other reported incidents makes it possible that the disruption seen in the Berezovets track was a result of the GPS interference known to be occuring in the area. 

Two other vessels were also in areas with documented GPS disruptions, Suha Queen II approaching the coast of Libya, and Haj Sayed I transiting from the Suez canal to Eastern Turkey. However, in searching for vessels showing the same circling pattern seen over Point Reyes, I have not yet found that multiple vessels in areas like the Sea of Azov were similarly affected. Global AIS data does show a few vessels with tracks circling over other locations. Two pilot vessels on the Chilean coast had their broadcast positions suddenly jump to circling tracks over Madrid. The Suha Queen II approaching the coast of Libya had its track jump to the Chinese city of Shanwei. The most recent vessel to appear circling over Point Reyes is the Ting Yuk, a tugboat operating in Hong Kong, which had its AIS track disrupted for a few hours at the end of March. 

So far it remains a mystery why these circling AIS tracks are appearing specifically at Point Reyes and a few other locations. It’s tempting to speculate that there might be some connection to a major U.S. Coast Guard communication station in Point Reyes which was an important historic location for developing maritime communications technology. While the Coast Guard left the area several years ago, volunteers continue to maintain at Point Reyes the only operational ship-to-shore maritime radio station. Still, it’s unclear why this location would somehow appear on AIS trackers. The fact that individual vessels in many different locations have been affected is puzzling and it’s unknown if any of these examples reflect actual disruptions of the GPS system. However some studies, such as a yearlong cruise by researchers of the German Aerospace Center which measured instances of GPS interference even during high seas transits, indicate that we may still have a great deal to learn about the true extent of global disruptions to this critical navigation system.