Demonstration of applying Google's Satellite Embeddings Classifier to rapidly mapping ecosystems in the Philippines. 5-8th of May 2026

Lab member Alys Young contributed to a workshop focused on identifying, mapping, and assessing the status of the Philippines' ecosystems. Hosted by the Wildlife Conservation Society and led by the IUCN, the workshop trained participants in the Global Ecosystem Typology, the Global Ecosystems Atlas, and the IUCN Red List of Ecosystems. 

The Red List of Ecosystems is the global standard for assessing ecosystem conservation status. Ecosystem mapping supports RLE assessments by providing information on the spatial extent of ecosystems and changes in their distributions over time. Alys demonstrated the modelling approaches of the Global Ecosystem Atlas to understand potential approaches for a national scale. Along with open datasets and software, the Global Ecosystem Atlas is currently developing reflections and guidance on how to collect ecosystem occurrence points and bring together multiple maps.

The Philippines is one of the most biodiverse regions on Earth. Ecosystem mapping and Red List assessments are important steps forward for not only national decision-making and reporting on international agreements, but also for global biodiversity conservation. We are proud to contribute to such initiatives and look forward to seeing developments from the Philippines.

Workshop instructions

Step 1. Open the app and find a small area 

Navigate to the Satellite Embeddings Classifier application: https://tinyurl.com/map-ecosystems

Select an area of interest or a small geographic region for which you know well and want to develop a map of ecosystems. We suggest a small area to start. Once you're familiar with the tool, you can map a larger area.

Step 4. Develop your training data

Now we must place points on the map for each of these classes.

Click on the name of the class and then on the + button on the right of the name. 

Add 5-10 points per class. Use your knowledge of an area and of the ecosystem to find places where you are confident you know that the ecosystem occurs.

Repeat this for each class.

Once you have added some points for at least two classes, the model will automatically run and begin to make a map. You can turn this map on and off by clicking on the 'layers' button on the top left and checking or unchecking 'Classification'. 

Step 5. Refine your map through iteration

Now that you have your first draft map, you can begin the process of improvement. Typically, you would continue to work through refining your training data until you cannot make your map any more accurate.

1. • Add more training points, aiming for a well-distributed set of training data that covers all of the ecosystem functional groups in your map. 

   • Add more points in places in your map where your first draft is not correct. See the images below for adding more urban points where the original map did not predict.

As you continue to add new training data, your map will slowly improve until you're unable to make any further improvements.

Notes and advice

Here are a few final tips to advance your map using the Satellite Embeddings Classifier:

1. • The map will only be as good as the training data, so try to be accurate with your training data.

   • You can try 'lumping' very similar classes. When classes such as two different types of wetland, or even oceanic versus lakes, it's often better to lump them into a single 'water' class. After making your map, we can then do a post-processing analysis to 'split' those classes back into Ecosystem Functional Groups.

   • Some ecosystems, such as tropical flooded forests, are extremely difficult to map using Earth observation data alone. Therefore, in these situations, it is often necessary to 'lump' similar forest types and then implement a post-processing analysis to split them back into particular ecosystem types.

Ecosystem occurrence points are a valuable resource for training these models. Investment and rigorous methods are important to ensure that the data is the most accurate and can achieve the best map.