Google recently announced Earth Engine Noncommercial Tiers for Google Earth Engine. This is a big change that affects all the non-commercial users of the GEE platform. Before the change, if you qualified for non-commercial use, you could use Earth Engine without any restrictions or fees. With the introduction of non-commercial tiers, you now have monthly limits on how much compute you can use for free. Once you exceed the allocated monthly quota, your account will enter a Restricted mode that will slow down the computations triggered through your account.

See our updated Sign-up for Google Earth Engine page with guidance on how to select your quota tier.

If you are a non-commercial user of Earth Engine, you now need to monitor and manage your quota usage to ensure you comply with these limits. This post outlines the concepts and tools you can use for quota monitoring.

The post covers the following topics:

• Understanding Earth Engine Compute Unit (EECU)
• Monitoring Quota using Google Cloud Console
• Monitoring Quota using Python (using Google Colab)

Understanding Earth Engine Compute Unit (EECU)

The basic unit of quota measurement is an EECU-hour. Since Earth Engine is a parallel-computing system, your script can use many different machines in parallel to accomplish your task. If your code runs on just 1 cpu on a single machine and takes 1 hour to complete, it will consume 1 EECU-hour of quota. Since Earth Engine typically distributes your computation across many machines, the total quota consumed will be the sum of usage across all the machines.

Checking your EECU Usage

When executing any script via the Code Editor, you can monitor the compute usage by using the Run with Profiler option. This shows how many EECU-seconds (3600 EECU-seconds = 1 EECU-hour) are used by different parts of your script. Similar can be done via the Python API using the ee.profilePrinting() API. Profiler is especially useful to determine which parts of the code are consuming more quota and improve the efficiency of your workflows.

However, you know that most compute-intensive tasks in GEE are run via the batch processing system using Exports. After completion of an export task, the total EECUs consumed are shown in the Tasks tab. As you can see below, the example export task shown here took 4 minutes to run but consumed 124089 EECU-seconds (34.47 EECU-hours) as the computation was distributed across many machines.

The above methods are useful in understanding the compute usage for your individual scripts and tasks. For ongoing monitoring and calculating the aggregate usage of your monthly quota, you need to use Google Cloud Console.

Monitoring Quota Using Google Cloud Console

Google Cloud Console is a unified platform to manage services in Google Cloud. As Earth Engine is now a Google Cloud offering, Earth Engine usage can be monitored with the standard cloud monitoring tools. The Monitoring Usage page of the GEE User Guide explains different components of the system. Here, I want to focus on monitoring the total monthly quota consumed by your account. This is the single most useful metric that users need to track to ensure they stay within the allocated quota.

Open the Metrics Explorer in Google Cloud Console. Verify that the selected project name is the one associated with Google Earth Engine. Next, click the Pick time range selector and set the monitoring time period. Since the quota is enforced on a rolling- monthly basis, typically you want this to be the beginning of the month to the current date.

Next, click on the Metric dropdown. The main metric of interest is the total EECUs consumed, so navigate to Earth Engine Cloud Project → Project → Completed EECU-seconds.

You can look at the quota consumption in aggregate or split by different usage types. It is useful to see the quota use by online (code editor/Python scripts) and batch (export tasks). Set the Aggregation to Sum by compute_type.

Strangely, the default reporting of the metrics is by EECU-seconds used per second. This is not useful at all. We can change it to report total EECU-seconds over the time period. Click on the Sum dropdown and select Configure aligner.

Change the Alignment function to be Sum.

Finally, we change the Display to be a Table.

The numbers displayed will be the total quota consumed by Earth Engine in the chosen time period. Depending on the number, the unit may be seconds, hours or days. In the example dashboard shown below, the total quota consumed is 53.482 EECU-hours (44.92 batch + 8.562 online).

Monitoring Quota using Python

The Metrics explorer UI is a bit hard to navigate. To allow you to easily monitor the quota, Google has also provided a Colab Notebook that helps you visualize historical and current EECU usage. You just need to add your project id and run the notebook to see monthly aggregated usage.

Once you run the notebook, you will be asked sign-in to your account. After authenticating, the notebook will fetch the usage data and display a nice chart that will show you your usage over the past year.

Conclusion

It is a new era for Google Earth Engine. Being freely available for over a decade, GEE has powered many ground-breaking scientific advances in Remote Sensing and has become an essential tool for researchers. While the new quota limits are generous and well-intended (to prevent abuse) along with a path to request more quota for high-impact users – it does raise new concerns about the viability of relying on GEE as the only computing platform for many users.

If you are a remote sensing practitioner or researcher, your toolkit should evolve beyond GEE to include other cloud-native platforms and approaches. For the past few years, I have been recommending XArray + Dask as the open-source stack for large-scale computation, with XEE offering great interoperability with the GEE ecosystem.