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How Brazil’s first AI for Earth grantee plans to use AI to better conserve precious rivers

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Fresh water is an essential resource. Brazil has more than any other country but millions of its people still don't have access to clean and safe water sources.

Sewage, run-off and industrial pollution contaminate many of the rivers that flow through Brazil's Atlantic Forest region. This vast area, stretching from Natal in the north to the borders with Paraguay and Argentina in the south, is home to the country's biggest cities and the majority of its urban population.

Poor-quality water can bring disease, exacerbate poverty and hamper economic development - and the harmful effects aren't restricted to humans. The Atlantic Forest region includes endangered ecosystems that are some of the richest areas of biodiversity in the world. Some 20,000 varieties of plants and hundreds of species of mammals, birds, reptiles, amphibians and fish live in habitats ranging from dense mangroves to high grasslands.
[Subscribe to Microsoft on the Issues for more on the topics that matter most.] For more on how Microsoft is putting cloud and AI tools in the hands of those working to solve global environmental challenges, visit AI for Earth. And follow @MSFTIssues on Twitter
Since 1986, SOS Mata Atlantica has been working to protect these ecosystems and the people of the Atlantic Forest. As part of its clean water initiative, 3,500 volunteers monitor 250 rivers, continually testing water quality and logging their results.

In this video, volunteers explain how a grant from Microsoft's AI for Earth program will help them gather and share data in order to influence public policy and preserve a vital resource.

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How clean water data is helping to protect Brazil's precious ecosystems | Microsoft On the Issues

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Since 1986, SOS Mata Atlantica has been working to protect Brazil's vital yet fragile ecosystem as part of its clean water initiative. In this video, volunteers explain how a grant from Microsoft's AI for Earth program helps them gather data in order to preserve a vital resource.

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Washington D. C., Distrito de Columbia, EE. UU.

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a machine learning algorithm that analyzes seismograms to determine whether the seismic activity is an earthquake or simply low-level noise

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Data

The state of Oklahoma, USA has recently experienced a marked surge in seismic activity (1, 10, 20) that has been correlated with the intensification of waste water injection (21-24). Here, we focus on the particularly active area near Guthrie, OK. In this region, the Oklahoma Geological Survey (OGS) cataloged 2021 seismic events from 15 February 2014 to 16 November 2016 (see Fig. 1). Their seismic moment magnitudes range from M w −0.2 to M w 5.8. We use the continuous ground velocity records from two local stations GS.OK027 and GS.OK029 (see Fig. 1). GS.OK027 was active from 14 February 2014 to 3 March 2015. GS.OK029 was deployed on 15 February 2014 and has remained active since. Signals from both stations are recorded at 100 Hz on three channels corresponding to the three spatial dimensions: HHZ oriented vertically, HHN oriented north-south, and HHE oriented west-east.

Generating location labels

We partition the 2021 earthquakes into six geographic clusters. For this, we use the K-means algorithm (25), with the Euclidean distance between epicenters as the metric. The centroids of the clusters we obtain define six areas on the map ( Fig. 1). Any point on the map is assigned to the cluster whose centroid is the closest (that is, each point is assigned to its Voronoi cell). We find that six clusters allow for a reasonable partition of the major earthquake sequences. Our classification thus contains seven labels or classes in the machine learning terminology: Class 0 corresponds to seismic noise without any earthquake, and classes 1 to 6 correspond to earthquakes originating from the corresponding geographic area.

Extracting windows for classification

We divide the continuous waveform data into monthly streams. We normalize each stream individually by subtracting the mean over the month and dividing by the absolute peak amplitude (independently for each of the three channels). We extract two types of 10-s-long windows from these streams: windows containing events and windows free of events (that is, containing only seismic noise).

To select the event windows and attribute their geographic cluster, we use the catalogs from the OGS. Together, GS.OK027 and GS.OK029 yield 2918 windows of labeled earthquakes for the period between 15 February 2014 and 16 November 2016. Benz et al. (10) built a new comprehensive catalog of events between 15 February and 31 August 2014 but have not yet provided earthquake location, which are needed for training.

We look for windows of seismic noise in between the cataloged events. Because some of the low-magnitude earthquakes may be buried in seismic noise, it is important that we reduce the chance of mislabeling these events as noise. We thus use a more exhaustive catalog created by Benz et al. (10) to select our noise samples. Our process yields 831,111 windows of seismic noise.

Training/testing sets

We split the window data set into two independent sets: a test set and a training set. The test set contains all the windows for July 2014 (209 events and 131,072 windows of noise), whereas the training set contains the remaining windows (2709 events and 700,039 noise windows).

Data set augmentation

Deep classifiers, such as ours, have many trainable parameters. Although they require a large amount of examples of each class to avoid overfitting, they generalize correctly to unseen examples. To build a large enough data set of events, we use streams recorded at two stations (GS.OK029 and GS.OK27; see fig. S1), thus roughly doubling the number of windows in the original training set. The input of our network is the three-channel raw waveform (seen as a single stream) recorded at either of these stations. Furthermore, we generate additional event windows by perturbing existing ones with zero-mean Gaussian noise. This balances the number of event and noise windows during training, a strategy to regularize the network and prevent overfitting (26-29).

ConvNetQuake

Our model is a deep convolutional network ( Fig. 2) that takes a window of three-channel waveform seismogram data as input and predicts its label either as seismic noise or as an event with its geographic cluster. The parameters of the network are optimized to minimize the discrepancy between the predicted labels and the true labels on the training set (see Methods for details).

Detection accuracy

In a first experiment to assess the detection performance of our algorithm, we ignore the geographic label (that is, labels 1 to 6 are considered as a single "earthquake" class). The detection accuracy is the percentage of windows correctly classified as earthquake or noise. Our algorithm successfully detects all the 209 events cataloged by the OGS. Among the 131,972 noise windows of our test set of July 2014, ConvNetQuake correctly classifies 129,954 noise windows and misclassifies 2018 of the noise windows as events. Among those windows, 1902 windows were confirmed as events by the autocorrelation method (detailed in the Supplementary Materials). That is, our algorithm made 116 false detections. In summary, our algorithm predicts 129,954 true negatives, 116 false positives, 0 false negative, and 2111 true positives. Therefore, the precision (fraction of detected events that are true events) is 94.8%, and the recall (fraction of true events correctly detected) is 100%.

Location accuracy

We then evaluate the location performance. For each of the detected events, we compare the predicted class (1-6) with the geographic label chosen from the OGS catalog. We obtain 74.5% location accuracy (fraction of correctly labeled class) on the test set (see Table 1). For comparison with a "chance" baseline, selecting a class at random would give 1/6 = 16.7% accuracy.

We also experimented with a larger number of clusters (50; see fig. S2) and obtained 22.5% in location accuracy. Although the accuracy for higher-resolution location is lower, it remains 10 times better than a chance at 1/50 = 2%. The performance drop is not surprising because, on average, each class now only provides 40 training samples, which we attribute to an insufficient number of labels for proper training.

Probabilistic location map

Our network computes a probability distribution over the classes. This allows us to create a probabilistic map of earthquake location. We show in Fig. 3 the maps for a correctly located event and an erroneous classification. For the correctly classified event, most of the probability mass is on the correct class. This event is classified with approximately 99% confidence. For the misclassified event, the probability distribution is more diffuse, and the location confidence drops to 40%.

Generalization to nonrepeating events

Our algorithm generalizes well to waveforms that are dissimilar from those in the training set. We quantify this using synthetic seismograms and compare our method to template matching (5). We generate day-long synthetic waveforms by inserting multiple 45 copies of a given template over a Gaussian noise floor, varying the signal-to-noise ratio (SNR) from −1 to 8 dB. An example of synthetic seismogram is shown in fig. S3.

We choose two templates waveforms T ₁ and T ₂ (shown in fig. S4). Both waveforms T ₁ and T ₂ exhibit opposite polarity (either due to a different location or source focal mechanism) and pulse duration (event size). Using the procedure described above, we generate a training set using T ₁ and two testing sets using either T ₁ or T ₂. We train both ConvNetQuake and the template matching method (see the Supplementary Materials) on the training set (generated with T ₁).

On the T ₁ testing set, both methods successfully detect all the events. On the other testing set where only T ₂ is present, the template matching method fails to detect the inserted events, even at high SNR. However, ConvNetQuake recognizes the new (unknown) events. The accuracy of our model increases with SNR (see Fig. 4). For SNRs higher than 7 dB, ConvNetQuake detects all the inserted seismic events.

This generalization of waveform recognition is also highlighted in our test set with real seismic waveforms. Some events in our test data set from Oklahoma are nonrepeating events (see highlighted events in Fig. 1). Although a template matching method using the waveforms from our training set cannot detect them, ConvNetQuake detected them using its compact and general representation of waveform features.

Earthquake detection on continuous records

We run ConvNetQuake on 1 month of continuous waveform data recorded at GS.OK029 in July 2014. The three-channel waveforms are cut into 10-s-long, nonoverlapping windows, with a 1-s offset between consecutive windows to avoid potential redundant detections. Our algorithm detects 4225 events in addition to those from the OGS catalog. This is more than five events per hour. Performing autocorrelation confirms 3949 of these are events (see the Supplementary Materials for details). One set of these events that is repeated 479 times (either highly correlated or highly anticorrelated) is shown in Fig. 5. For comparison, the subspace detection method (10) using three-channel templates found 5737 events during this period.

Comparison with other detection methods

We compare our detection performances to autocorrelation and FAST reported by Yoon et al. (11). Autocorrelation relies on waveform similarity (repeating earthquakes), and FAST relies on fingerprints similarity. Both techniques do not require a priori knowledge on templates, and they provide a detection but no location. Because FAST generates a new database of template through feature extraction and is more computationally efficient than autocorrelation, it is particularly suited for regions where earthquakes are poorly cataloged.

Yoon et al. (11) applied FAST to detect new events during 1 week of continuous waveform data recorded at a single station with a single channel from 8 January 2011 to 15 January 2011 in northern California and compared with the autocorrelation method. Twenty-four earthquakes occurred during this period: a M w 4.1 that occurred on 8 January 2011 on the Calaveras Fault (North California) and 23 of its aftershocks ( M w 0.84 to M w 4.10, a range similar to our data set). Although the earthquake catalogs and locations are different, the comparison solely focuses on the algorithm performances given by the seismic data. Table 1 reports the classification accuracy of all three methods on the same duration of continuous time series (1 week). The main difference in the data sets is that the reported values of FAST and of the autocorrelation are for single-channel seismograms (ConvNetQuake on three channels), 1 week of continuous time series sampled at 20 Hz (ConvNetQuake uses 100-Hz data). The values of precision and recall are reported.

We further test the performance of our algorithm against a highly optimized template matching algorithm, EQcorrscan (version 0.1.4; https://github.com/eqcorrscan). Given that EQcorrscan requires ~128 GB of memory to detect events using only 100 three-channel, 3-s-long templates sampled at 100 Hz, we limit the exercise to six continuous days of July 2014. Within this period, 10 events are cataloged by OGS, including the two nonrepeating events highlighted in Fig. 1. Using the 2709 templates to train both ConvNetQuake and EQcorrscan, we find that ConvNetQuake correctly detects all of the events, whereas EQcorrscan only finds 4 of 10 events.

Scalability to large data sets

ConvNetQuake is highly scalable and can easily handle large data sets. The runtimes of the autocorrelation method, FAST, and ConvNetQuake necessary to analyze 1 week of continuous waveform data are also reported in Table 1. Our reported runtime excludes the offline training phase. This overhead is performed only once and took 1.5 hour on a NVIDIA Tesla K20Xm graphics processing unit. For the test set, we ran ConvNetQuake on a dual core Intel i5 2.9 GHz central processing unit. Similarly, FAST's runtime reported in Table 1 excludes the time required to build the database of templates (feature extraction) and only includes the similarity search. Although not directly comparable because of the differences in sampling rates and number of channels involved, ConvNetQuake is a fast algorithm. Excluding the training phase, it is approximately 13,500 times faster than autocorrelation and 48 times faster than FAST ( Table 1). The runtimes for long time series also indicate that ConvNetQuake presents an almost-linear scaling between runtime and duration of the time series to analyze (similar to FAST). For the 1-month-long continuous time series, ConvNetQuake runtime is 4 min and 51 s, whereas that of FAST is 4 hours and 20 min (see Fig. 6A).

Like other template matching techniques, FAST's database grows as it creates and stores new fingerprints during detection. For 2 days of continuous recording, FAST's database is approximately 1 GB (see Fig. 6B). Methodologies that require growing databases of templates eventually see their performance decreasing with data size. Our network only needs to store a compact set of parameters (the weights of the filters), which entails a constant memory usage (500 kB; see Fig. 6B).

Network architecture

The network's input is a two-dimensional (2D) tensor representing the waveform data of a fixed-length window. The rows of Z ⁰ for c ∈ {1, 2, 3} correspond to the channels of the waveform, and because we used 10-s-long windows sampled at 100 Hz, the time index is t ∈ {1, ..., 1000}. The core of our processing was carried out by a feed-forward stack of eight convolutional layers ( Z ¹ to Z ⁸), followed by one fully connected layer (1) where σ(⋅) = max(0,⋅) is the nonlinear ReLU (rectified linear unit) activation function. The output and input channels are indexed with z that outputs class scores. All the layers contain multiple channels and are thus represented by 2D tensors. Each channel of the eight convolutional layers was obtained by convolving the channels of the previous layer with a bank of linear 1D filters, summing, adding a bias term, and applying a pointwise nonlinearity, as follows c and c′, respectively, and the time dimension is indexed with t and t′. C i is the number of channels in layer i. We used 32 channels for layers 1 to 8, whereas the input waveform (layer 0) had three channels. We stored the filter weights for layer i in a 3D tensor W i with dimensions C i−1 × C i × 3. That is, we used three-tap filters. The biases were stored in a 1D tensor b i. All convolutions used zero padding as the boundary condition.

Equation 1 shows that our formulation slightly differs from a standard convolution: We used strided convolutions with stride s = 2; that is, the kernel slides horizontally in increments of two samples (instead of one). This allowed us to downsample the data by a factor of 2 along the time axis after each layer. This was equivalent to performing a regular convolution, followed by subsampling with a factor of 2, albeit more efficiently.

Because we used small filters (the kernels have size 3), only the first few layers had a local view of the input signal and could only extract high-frequency features. Through progressive downsampling, the deeper layers had an exponentially increasing receptive field over the input signal (by indirect connections). This allowed them to extract low-frequency features (cf. Fig. 2).

After the eighth layer, we vectorized the tensor Z ⁸ with shape (4, 30) into a 1D tensor with 128 features . This feature vector was processed by a linear, fully connected layer to compute class scores z c with c = 0, 1, ..., (2) Owing to this fully connected layer, the network learned to combine multiple parts of the signal (for example, M − 1 given by P waves, S waves, and seismic coda) to generate a class score and could detect events anywhere within the window.

Finally, we applied the softmax function to the class scores to obtain a properly normalized probability distribution, which could be interpreted as a posterior distribution over the classes conditioned on the input Z ⁰ and the network parameters W and b (3) W = { W ¹, ..., W ⁹} is the set of all the weights, and b = { b ¹, ..., b ⁹} is the set of all the biases.

Compared to a fully connected architecture, such as that of Kong et al. (18) (where each layer would be fully connected as in Eq. 2), convolutional architectures, such as ours, are computationally more efficient. This efficiency gain was achieved by sharing a small set of weights across time indices. For instance, a connection between layers Z ¹ and Z ², which have dimensions of 500 × 32 and 250 × 32, respectively, requires 3072 = 32 × 32 × 3 parameters in the convolutional case with a kernel of size 3. A fully connected connection between the same layers would entail 128, 000, 000 = 500 × 32 × 250 × 32 parameters, a four orders of magnitude increase.

Furthermore, models with many parameters require large data sets to avoid overfitting. Because labeled data sets for our problem are scarce and costly to assemble, a parsimonious model such as ours is desirable.

Training the network

We optimized the network parameters by minimizing an L ₂-regularized cross-entropy loss function on a data set of N windows indexed with k (4)

The cross-entropy loss measures the average discrepancy between our predicted distribution p(k) and the true class probability distribution q(k) for all the windows k in the training set. For each window, the true probability distribution q(k) has all of its mass on the window's true class (5)

To regularize the neural network, we added an L ₂ penalty on the weights W, balanced with the cross-entropy loss via the parameter λ = 10 −3. Regularization favored network configurations with small weight magnitude. This reduced the potential for overfitting (31).

Because both the parameter set and the training data set were too large to fit in memory, we minimized Eq. 4 using a batched stochastic gradient descent algorithm. We first randomly shuffled the N = 702, 748 windows from the data set. We then formed a sequence of batches containing 128 windows each. At each training step, we fed a batch to the network, evaluated the expected loss on the batch, and updated the network parameters accordingly using backpropagation (13). We repeatedly cycled through the sequence until the expected loss stopped improving. Because our data set was unbalanced (we had many more noise windows than events), each batch was composed of 64 windows of noise and 64 event windows.

For optimization, we used the ADAM (32) algorithm, which kept track of first- and second-order moments of the gradients and was invariant to any diagonal rescaling of the gradients. We used a learning rate of 10 −4 and kept all other parameters to the default value recommended by the authors. We implemented ConvNetQuake in TensorFlow (30) and performed all our trainings on a NVIDIA Tesla K20Xm graphics processing unit. We trained for 32,000 iterations, which took approximately 1.5 hours.

Evaluation on an independent testing set

After training, we tested the accuracy of our network on windows from July 2014 (209 windows of events and 131,972 windows of noise). The class predicted by our algorithm was the one whose posterior probability p c was the highest. We evaluated our predictions using two metrics. The detection accuracy is the percentage of windows correctly classified as events or noise. The location accuracy is the percentage of windows already classified as events that have the correct cluster number.

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/2/e1700578/DC1

section S1. Generalization ability of ConvNetQuake

section S2. Autocorrelation for detection of new events during July 2014

fig. S1. Waveform of an event recorded with station GS.OK029 and GS.OK027.

fig. S2. Earthquakes in the region of interest (near Guthrie, OK, USA) from 14 February 2014 to 16 November 2016 partitioned into 50 clusters.

fig. S3. Day-long synthetic seismic record constructed by inserting the waveform template shown in fig. S4A at random times into a time series of Gaussian noise.

fig. S4. Templates used to generate synthetic data.

fig. S5. Distribution of the correlation coefficients after autocorrelation of the windows classified as events by ConvNetQuake.

fig. S6. Waveforms of the events detected in cluster 3 using a correlation coefficient threshold of 0.1.

fig. S7. Waveforms of the events detected in cluster 3 using a correlation coefficient threshold of 0.2.

fig. S8. Waveforms of the events detected in cluster 3 using a correlation coefficient threshold of 0.3.

View Abstract

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Convolutional neural network for earthquake detection and location

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The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for efficient algorithms to reliably detect and locate earthquakes.

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Moulton Niguel Water District, Aliso Viejo Parkway, Aliso Viejo, California, EE. UU.

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Moulton Niguel Water District, California, USA

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Moulton Niguel Water District, California, USA

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Develop a “proof of concept” water demand forecasting model

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DataKind San Francisco-Bay Area (DataKind SF) launched in August 2014 with the vision of creating examples of successful efforts that bring tech and public good sectors together. In a region well known for attracting top tech talent from around the globe and for its incredible community of data scientists and data enthusiasts, DataKind SF hopes to tap into this community's desire to have a greater impact both locally and globally. Although the universities, community and culture of the Bay Area have always fostered nonprofits striving for positive change, costs are rising rapidly. Through DataKind, they hope to execute data projects that are often out of reach for organizations that could most greatly benefit from them. With a growing group of core volunteers, DataKind SF is inspired by the opportunity to transform how nonprofits operate by using data more effectively.

Meet the Team

Abhishek Kapatkar

Abhishek is an engineer with a background in computer science and product development. Currently, he is helping Netflix build its new data science and machine learning infrastructure. He cares deeply about promoting equality, diversity and social justice. Abhishek joined DataKind because he believes greatly in the potential to harness the power of data science in service of humanity. Outside of work, he enjoys running, traveling, noshing on street food and training for endurance cycling events.

© 2012-2021 DataKind, Inc. All Rights Reserved. Privacy Policy

# data[].attributes.attachment_link.description
DataKind San Francisco-Bay Area (DataKind SF) launched in August 2014 with the vision of creating examples of successful efforts that bring tech and public good sectors together. In a region well known for attracting top tech talent from around the globe and for its incredible community of data scientists and data enthusiasts, DataKind SF hopes to tap into this community's desire to have a greater impact both locally and globally.

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77 Van Ness Avenue, San Francisco, California, EE. UU.

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77 Van Ness Avenue, San Francisco, California, EE. UU.

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77 Van Ness Avenue, San Francisco, California, EE. UU.

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Rain Forest Connection repurposes old phones to help protect the forest from illegal logging

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Alto Mayo, Peru

The biodiversity of Peru's Alto Mayo rainforest is slowly being whittled away by systemic illegal logging, poaching, and drug trafficking. Working in conjunction with Conservation International (CI) and Peruvian government rangers, Rainforest Connection is testing an acoustic alert system that listens for telltale indicators of deforestation (chainsaws) or animal poaching (motorcycles) - as well as for trucks and vehicles that indicate narco-trafficking.

Cerro Blanco, Ecuador

One of Ecuador's most diverse rainforest preserves, Cerro Blanco lies in close proximity to the sprawling metropolis of Guayaquil. As a result, it faces threats from deforestation, poaching, and urban encroachment. In concert with Fundacíon Pro Bosque (FPB), RFCx has set up a system to monitor for illegal chainsaws and poaching in real-time. In addition to helping better protect 10,000 hectares of rainforest, our system also streams 1.8 gigabytes of data per day, giving scientists and conservationists a means of keeping track of endangered wildlife. This project includes real-time alerts and data analysis for several species of rare parrots, assisting local rangers and biologists by helping them monitor endangered species and safeguard their habitat from poachers.

Tembé Tribal Reserve, Northern Brazil

Due to scarcity of numbers, the Tembé people of northern Brazil have found it difficult to put an end to large scale illegal logging, poaching, and drug smuggling operations in remote areas of their ancestral rainforests. Based on our 2015 pilot, the Tembe have asked RFCx to deploy a permanent system to help them protect the rainforest reserve - a monitoring system that can act as a force multiplier for a handful of trained Tembe Rangers. Our 2018/19 focus includes deploying and expanding this system to alert Tembé Rangers to indications of illegal logging (chainsaws), poaching (motorcycles), and smuggling (trucks/vehicles). Moreover, our system has already created a host of wifi hotspots in remote areas that has allowed the tribe to communicate more effectively among themselves - as well as with the outside world, erasing the digital divide.

Cameroon

Home to some of the world's most richly dense rainforests, Cameroon is no stranger to illegal logging and poaching at an industrial scale. In partnership with the Zoological Society of London (ZSL) and a local sustainable logging company named Rougier, Rainforest Connection set up an acoustic monitoring to demonstrate our system could assist local park rangers in detecting unauthorized activities in protected areas that included the poaching of chimpanzees, gorillas, elephants and a host of other endangered birds.

Sumatra

Illegal deforestation continues to endanger the native habitat of Sumatra's gibbons - even in designated nature preserves. Working in concert with the Kalaweit Supayang Gibbons Reserve, Rainforest Connection pioneered an acoustic monitoring system that could detect the sound of chainsaws in the cacophony of the jungle - and instantly alert park rangers to the source of the noise. Right from the start, the results were remarkable: For the first time ever, staff members on the Reserve found themselves able to confront illegal loggers live and in person - and shut down their operations.

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Rainforest Connection | Protecting rainforests with real-time data

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The biodiversity of Peru's Alto Mayo rainforest is slowly being whittled away by systemic illegal logging, poaching, and drug trafficking. Working in conjunction with Conservation International (CI) and Peruvian government rangers, Rainforest Connection is testing an acoustic alert system that listens for telltale indicators of deforestation (chainsaws) or animal poaching (motorcycles) - as well as for trucks and vehicles that indicate narco-trafficking.

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Smithsonian National Museum of Natural History, Constitution Avenue Northwest, Washington D. C., Distrito de Columbia, EE. UU.

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eMammal stores and organizes images from citizen-run camera traps while learning more about the world’s land mammals

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eMammal stores and organizes images from citizen-run camera traps while learning more about the world’s land mammals

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Challenge

What does it mean for the ecosystem when invasive feral pigs tear through an area? What is the correlation between deer populations and spreading disease? Researchers hope to answer questions like these by studying local wildlife, but their organizations are critically underfunded-and they need help. Citizen scientists have the means to record valuable photos and data to help scientists tackle these complex conservation challenges. Their contributions are crucial to understanding and rehabilitating the world's land mammal population.

# data[].attributes.attachment_link.description
What does it mean for the ecosystem when invasive feral pigs tear through an area? What is the correlation between deer populations and spreading disease? Researchers hope to answer questions like these by studying local wildlife, but their organizations are critically underfunded-and they need help.

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Trial Watch, a project by the Clooney Foundation For Justice

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Justice for Victims of Unfair Trials TrialWatch®

What is TrialWatch?
Courts are increasingly being used as a tool of oppression. In many countries, prosecutors and judges are used to imprison government critics and minorities.
In other places, a judge's rulings can be purchased by the highest bidder. Judges can also be complicit in grave human rights abuses when they convict for 'crimes' such as homosexuality or blasphemy, or when they ignore the due process rights of defendants. Yet judges and prosecutors are rarely held to account. In some countries, courtrooms are closed. And even where trials are open to the public, proceedings can be long, convoluted and hard to understand.

In response to these pressing needs, the Clooney Foundation for Justice has developed an initiative focused on monitoring and responding to trials around the world that pose a high risk of human rights violations. As an esteemed U.S. Supreme Court Justice once noted, "sunlight is the best disinfectant." We will therefore monitor trials in which the law may be used to target a minority or silence a government critic, meaning that there is a likelihood of a politicized, unfair trial. We will work to expose injustice and rally support to secure justice for defendants whose rights have been violated.

We are partnering with the Office of the United Nations High Commissioner for Human Rights, the American Bar Association, Columbia Law School, and Microsoft Corporation, to achieve TrialWatch's objectives.

Through the TrialWatch project, the Clooney Foundation for Justice will:

1. Recruit and train new trial monitors

TrialWatch has partnered with the Office of the High Commissioner for Human Rights to develop an interactive trial monitoring training based on international human rights standards. The training program and materials, are available here (and soon will be available in multiple languages). We have also partnered with the American Bar Association to recruit and train trial monitors, including non-lawyers, who can observe and report on criminal trials around the world.

2. Shed light on what is happening in courts all over the world

Trial monitors will be deployed to observe and report on criminal trials around the world. These monitors will be equipped with a specialized app we have developed with Microsoft that will facilitate their reporting in a consistent way.

3. Advocate for Justice

TrialWatch will work with legal experts to assess the fairness of trials according to international human rights standards. We will share the reports with international lawyers, journalists, diplomats and members of civil society who can advocate on behalf of the defendants and where appropriate we will conduct or fund legal advocacy to assist a defendant in pursuing remedies in regional or international human rights courts.

4. Develop the Global Justice Ranking™

TrialWatch will gather data that will contribute to the Global Justice Ranking™, an index documenting national courts' adherence to human rights and fair trial standards. This will evaluate countries according to the fairness of their justice system based on the data that is gathered through TrialWatch monitoring.

Visit our TrialWatch Q&A to learn more about TrialWatch.

Where and What We Monitor

TrialWatch is global in scope and focused on trials targeting journalists, LGBTQ persons, women and girls, minorities, and human rights defenders.

Click a region on the map below for cases we are monitoring around the world.

Trials we are monitoring or have monitored

Journalists

The trial in Morocco of Hajjar Raissouni, an opposition journalist who has alleged that her conviction and sentence for the 'crimes' of abortion and sex outside of marriage were a politically-motivated effort to silence her (and who was subsequently pardoned). See CFJ statement here. See full Fairness Report here.

The trial in Belarus of Marina Zolotova, the chief editor of the country's most popular online news outlet, who was prosecuted for criminal negligence due to her alleged failure to prevent employees from sharing passwords to paywalled content. See full Fairness Report here.

The trial in Turkey of Cansu Piskin, a legal reporter prosecuted under terrorism-related charges after she published the name of a prosecutor in an article about a case. See full Fairness Report here.

The trial in Cambodia of two former RFA journalists on espionage charges that carry a potential sentence of up to 15 years in prison. See full Fairness Report here and CFJ statement on the report's release here; see also CFJ statement on the two-year anniversary of their arrest here.

The trial in Nigeria of Omoyele Sowore, a journalist, former presidential candidate, and opposition critic who has been charged with treason, cyberstalking, and money laundering. See CFJ statement on his earlier detention here.

Proceedings in Nigeria against Samuel Ogundipe, who is being prosecuted for theft-related offenses - carrying a sentence up to seven years - in connection with reporting on confidential government documents.

Habeas corpus proceedings in India regarding Kishorechandra Wangkhem, who was detained in connection with Facebook posts denouncing the ruling party. See preliminary report by the American Bar Association's Center for Human Rights here.

The in absentia trial in Cambodia of two journalists, Aun Pheap and Peter Zsombor, on charges of incitement to commit a felony based on their reporting on voting patterns in a commune that the ruling party did not win in the last election.

The trial in Indonesia of journalist Muhammad Asrul, who has been charged with disseminating information that
incites hatred and purposefully causing public distress by disseminating false information, based on articles about alleged corruption by a local political figure.

The trial in the Dominican Republic of journalist Marino Zapete, who has been charged with criminal defamation for reporting on alleged corruption.

The trial in Uganda of journalist and documentary filmmaker Moses Bwayo on charges of unlawful assembly related to filming a documentary on an opposition figure.

The trial of Paul Chouta in Cameroon on charges of defamation, "publication of insulting language" and "false reporting as a cyber offense." See ABA preliminary report here.

The trials in Bangladesh of photojournalist and editor Shafiqul Islam Kajol, who has been charged with criminal defamation, deteriorating law and order, and the unlawful use of identity information in three separate cases related to a newspaper article he shared on social media that alleged a scandal involving Bangladeshi politicians.

The trial in Azerbaijan of opposition leader and former journalist Tofig Yagublu, who faces up to seven years imprisonment on hooliganism charges arising out of a car accident and alleged assault, which Mr. Yagublu states was staged.

The trial in Russia of journalist Svetalan Prokopyeva, who was charged and convicted of public justification of terrorism following statements she made on her radio program seeking to understand the root causes of a terrorist attack committed by a teenager. See CFJ statement here.

The trial of journalist Wan Noor Hayati Wan Alias in Malaysia, charged with disseminating false information that "could disrupt national stability and public order" for Facebook posts questioning the government's decision to allow tourists on a cruise ship coming from Wuhan to disembark at Penang.

The trial of a journalist in Africa charged with offenses including "spreading fake news" relating to their reporting on the government's response to COVID-19.

The trial of journalist Dhaval Patel in India, charged with sedition and spreading "false alarm or warning" for reporting on the handling of COVID-19 by the authorities in the state of Gujarat.

The trial of journalist Kufre Carter in Nigeria, charged with criminal defamation and conspiracy for an article that described a conversation between two unnamed health professionals in which they criticized the local health commissioner's response to the COVID crisis.

The trial of blogger Emna Chargui in Tunisia, who was convicted of "inciting hatred between religions through hostile means or violence" and "infringing an authorized religion" for a poem she re-posted on Facebook entitled 'Sura Corona,' which discussed the importance of science guiding the response to COVID in the style of a Quranic verse.

The trial in Turkey of journalists from OdaTV for allegedly disclosing the name of an intelligence officer, in violation of Turkish intelligence law, despite the fact that the information had already been made public by others.

The trial in Turkey of journalists from the news portal Diken on charges of 'aiding a terrorist organization without being its member' for covering tweets from the anonymous 'Fuat Avni' Twitter account.

The trial in Malaysia of online news site Malaysiakini and its editor-in-chief, Steven Gan, on charges of contempt for unmoderated comments posted by third parties on the news site.

The trial in Malaysia of blogger Dian Abdullah, who has been charged with sharing offensive content and making statements that could cause public mischief in connection with criticism of the Malaysian government's failure to provide sufficient assistance to the most vulnerable during the pandemic.

The trial in Morocco of Omar Radi, an investigative journalist who has previously focused on the financial holdings of the King of Morocco, on charges of insulting the judiciary. See CFJ statement on his conviction here. See CFJ statement here and full Fairness Report here.

LGBTQ Persons
We are researching the situation of LGBTQ persons in a number of other jurisdictions with a view to identifying trials for potential monitoring, for instance, collaborating with Cleary Gottlieb on research regarding LGBTQ persons in the Caribbean. We are also monitoring cases such as:

The trial in Uganda of 67 individuals arrested at an LGBTQ-friendly bar and charged with public nuisance.

The trial in Nigeria of 57 men arrested in a raid on a hotel and charged with same-sex public displays of affection.

Proceedings in Malawi against Gift Trapence and Reverend MacDonald Sembereka, who have been charged with, among other things, forgery of official documents for an LGBTI and sex workers workshop.

The trial in Uganda of Mohammed Mutumba and Swabullah Nabukeera on charges of "carnal knowledge with a person against the order of nature."

Women and Girls

Human Rights Defenders

The trial of Ahmed Manseri in Algeria, who was charged with criminal defamation in connection with his advocacy regarding human rights abuses perpetrated by the security forces. See full Fairness Report here.

The trial of six activists in Zambia charged with disobeying lawful orders in connection with a planned, peaceful protest against government corruption. See full Fairness Report here.

The trial of Veysel Ok in Turkey, who was convicted of insulting the judiciary on account of a press interview he gave questioning the judiciary's lack of independence. See full Fairness Report here; see CFJ statement on his conviction here.

The mass trial in Equatorial Guinea of over a hundred individuals in relation to their alleged involvement in a coup plot, in which 112 defendants were convicted, with 20 defendants receiving sentences of over 70 years in prison. See full Fairness Report here; see CFJ statement here.

The trial of Mikhail Benyash in Russia, a lawyer who has defended human rights activists and was convicted of assaulting the police, despite the fact that Benyash alleged that he was the one who was beaten and violently pushed. See CFJ statement here. See CFJ statement and full Fairness Report here.

The trial in Myanmar of seven members of the satirical theater group Peacock Generation Troupe, six of whom were convicted of statements intended to cause or likely to cause mutiny or disregard of duties in the Army on the basis of their performance of a traditional Thangyat. See CFJ statement here. See full Fairness Report here.

The trial in Guatemala of Abelino Chub Caal, an indigenous leader who was charged with aggravated usurpation, arson, and illicit association. See full Fairness Report here.

The trial in Thailand of Wut Boonlert, an indigenous activist charged with criminal defamation for sharing an article about a Parks official. See CFJ statement and full Fairness Report here.

The trial in Russia of Server Mustafayev, a Tatar human rights defender who has been charged with terrorism-related offenses. See CFJ statement here. See CFJ statement on his conviction here.

The trial in Zambia of Chishimba Kambwili, who has been charged with criminal defamation for comments allegedly about the President of Zambia.

The trial in Asia of lawyers in connection with human rights advocacy.

The trial in Thailand of four individuals charged with sedition and 'membership in a secret society' for their alleged affiliation with a political group that advocates for a republican-style government and which the Thai government considers anti-monarchist: specifically, the defendants were accused of distributing flyers and wearing and selling t-shirts related to this political group. See CFJ statement and full Fairness Report here.

Two trials in Thailand involving claims of criminal defamation by the Thammakaset poultry company based on tweets and retweets regarding alleged labor abuses by Thammakaset.

Several trials in Hong Kong of pro-democracy protestors and opposition leaders charged with organizing and participating in unauthorized assemblies related to several demonstrations in 2019 and 2020.

The trial of Kong Raiya in Cambodia, who has been charged with incitement for advertising t-shirts commemorating the death of an activist. See CFJ statement here.

The trial of Alnur Ilyashev in Kazakhstan on charges of 'spreading false information during an emergency' for social media posts criticized the authorities for corruption and incompetence, including in response to the COVID-19 pandemic. See CFJ statement here. See CFJ statement and amicus brief here. See CFJ statement on his upheld conviction here.

The trial of artist Danai Ussama in Thailand, charged with dissemination of "false" information for Facebook posts stating that neither he nor other passengers were given appropriate health screening at Bangkok airport upon their return from Barcelona.

The trial in Thailand of student activist Parit Chiwarak on charges of contempt for organizing a courthouse demonstration in support of other pro-democracy activists facing prosecution.

The trial in Thailand of Arnon Nampa and Panupong Jadnok on charges of sedition and other public-order offenses for their participation in anti-monarchy and anti-government protests. See CFJ statement on Arnon Nampa's arrest here.

Minorities
TrialWatch is actively tracking cases in which members of minority groups are prosecuted for who they are or what they believe. We are monitoring cases such as:

The trial in Indonesia of Suzethe Margaret, who was charged with blasphemy for behaving disrespectfully in a mosque during a period when she may have been suffering from mental illness.

The trial in Thailand of Wut Boonlert, an indigenous activist charged with criminal defamation for sharing an article about a Parks official. See CFJ statement and full Fairness Report here.

The trial in Russia of Server Mustafayev, a Tatar human rights defender who has been charged with terrorism-related offenses. See CFJ statement here. See CFJ statement on his conviction here.

The trial in Guatemala of Abelino Chub Caal, an indigenous leader who was charged with aggravated usurpation, arson, and illicit association. See full Fairness Report here.

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Justice for Victims of Unfair Trials - Clooney Foundation For Justice

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Courts are increasingly being used as a tool of oppression. In many countries, prosecutors and judges are used to imprison government critics and minorities. In other places, a judge's rulings can be purchased by the highest bidder.

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UNHCR Lebanon: Nimble, Collaborative Rapid Response

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UNHCR

The United Nations High Commissioner for Refugees (UNHCR) is a global refugee agency whose mission is to protect the rights of refugees as well as helping them work towards a brighter future. UNHCR's Inter-Agency Coordination Unit based in Beirut, Lebanon, is the epicenter of a complex web of refugee support and response where they coordinate 200 organizations.

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United Nations: Geospatial Rapid Response - CARTO

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The United Nations High Commissioner for Refugees (UNHCR) is a global refugee agency whose mission is to protect the rights of refugees as well as helping them work towards a brighter future. UNHCR's Inter-Agency Coordination Unit based in Beirut, Lebanon, is the epicenter of a complex web of refugee support and response where they coordinate 200 organizations.

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12 John Prince's Street, London W1G 0JR, Reino Unido

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12 John Prince's Street, London W1G 0JR, Reino Unido

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World Bee Project