title: "Satellite Embedding Deep Dive (Full Workshop)"

subtitle: "A hands-on workshop for working with the Satellite Embedding dataset in Google Earth Engine."

author: "Ujaval Gandhi"

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```{r echo=FALSE}

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Google [Satellite Embedding Dataset](https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL) is a global dataset of pre-computed, analysis-ready geospatial embeddings for each year from 2017 onwards. This dataset was produced by Google DeepMind's AlphaEarth Foundations model and presents a big leap in making remote sensing and climate datasets useful. Along with making existing remote sensing workflows more efficient, this dataset unlocks a range of new use cases. This workshop is designed to help you get started with the dataset in GEE and use it for a variety of applications.

[](https://www.youtube.com/watch?v=vDWjfW7pylM){target="_blank"}

[Watch the Video ↗](https://www.youtube.com/watch?v=vDWjfW7pylM){target="_blank"}

[Access the Presentation ↗](https://docs.google.com/presentation/d/1X0LodVyTO9ou68LiuRMAeUhPYaortMND2x-0Rb1LkzM/edit?usp=sharing){target="_blank"}

If you already have a Google Earth Engine account, you can skip this step.

Visit our [GEE Sign-Up Guide](gee-sign-up.html) for step-by-step instructions.

The workshop material and exercises are in the form of Earth Engine scripts shared via a code repository.

1. [Click this link](https://code.earthengine.google.co.in/?accept_repo=users/ujavalgandhi/GEE-Satellite-Embedding) to open Google Earth Engine code editor and add the repository to your account.

2. If successful, you will have a new repository named `users/ujavalgandhi/GEE-Satellite-Embedding` in the *Scripts* tab in the *Reader* section.

If you do not see the repository in the *Reader* section, click *Refresh repository cache* button in your *Scripts* tab and it will show up.

```{r echo=FALSE, fig.align='center', out.width='50%', fig.cap='Workshop Repository'}

The course is accompanied by a video covering the all the sections. This video is recorded from our live webinar class and is edited to make them easier to consume for self-study. We have 2 versions of the videos:

The video on YouTube is ideal for online learning and sharing. You may also turn on *Subtitles/closed-captions* and adjust the playback speed to suit your preference. [Access the YouTube Video ↗](https://youtu.be/vDWjfW7pylM){target="_blank"}

We have also made full-length video available on Vimeo. This video can be downloaded for offline learning. [Access the Vimeo Video ↗](https://vimeo.com/1115765849?share=copy){target="_blank"}

\newpage

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Visualizing a satellite embedding image with 3 clusters'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F01b_Visualizing_Embeddings_(complete)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/01b_Visualizing_Embeddings_(complete)')}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F01c_Visualizing_Embeddings_(exercise)){target="_blank"}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Detected Crop Type Map using Unsupervised Classification'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F02b_Unsupervised_Classification_(complete)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/02b_Unsupervised_Classification_(complete)')}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Crop Mapping Exercise with Satellite Embedding'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F02c_Unsupervised_Classification_(exercise)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/02c_Unsupervised_Classification_(exercise)')}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Mangrove Classification with Satellite Embedding Dataset'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F03b_Supervised_Classification_(complete)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/03b_Supervised_Classification_(complete)')[-(1:232)]}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F03c_Supervised_Classification_(exercise)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/03c_Supervised_Classification_(exercise)')}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Detecting Brick Kilns with Similarity Search'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F04b_Similarity_Search_(complete)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/04b_Similarity_Search_(complete)')[-(1:18)]}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Exercise to Map Solar Farms with Similarity Search'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3AHands_On%2F04c_Similarity_Search_(exercise)){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Hands_On/04c_Similarity_Search_(exercise)')[-(1:13)]}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Similarity Search to Detect Farm Ponds'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?accept_repo=users%2Fujavalgandhi%2FEnd-to-End-GEE&scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3ASupplement%2FSimilarity_Search_with_Area_Filtering){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Supplement/Similarity_Search_with_Area_Filtering')[-(1:26)]}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Similarity Search to Map Open Plots'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?accept_repo=users%2Fujavalgandhi%2FEnd-to-End-GEE&scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3ASupplement%2FSimilarity_Search_with_Negative_Examples){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Supplement/Similarity_Search_with_Negative_Examples')[-(1:34)]}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Mapping Surface Water with Unsupervised Clustering'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3ASupplement%2FUnsupervised_Classification_Surface_Water){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Supplement/Unsupervised_Classification_Surface_Water')}

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Mapping Different Types of Vegetation with Embeddings'}

[Open in Code Editor ↗](https://code.earthengine.google.com/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3ASupplement%2FSupervised_Classification_Urban_Tree_Cover){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Supplement/Supervised_Classification_Urban_Tree_Cover')[-(1:814)]}

We can calculate mean value of satellite embeddings within each polygon of a layer and cluster them based on similarity. This approach is useful to group regions by similarity. In the example below, we use the US Census Blocks dataset and cluster the blocks within a city based on satellite embeddings.

```{r echo=FALSE, fig.align='center', out.width='75%', fig.cap='Clustered Census Blocks by Mean Satellite Embeddings'}

[Open in Code Editor ↗](https://code.earthengine.google.co.in/?scriptPath=users%2Fujavalgandhi%2FGEE-Satellite-Embedding%3ASupplement%2FClustering_Census_Blocks){target="_blank"}

```{js eval=FALSE, code=readLines('code/gee_satellite_embedding/Supplement/Clustering_Census_Blocks')}

* Official Tutorials in Google Earth Engine User Guide

* [Introduction to the Satellite Embedding Dataset](https://developers.google.com/earth-engine/tutorials/community/satellite-embedding-01-introduction)

* [Unsupervised Classification with Satellite Embedding Dataset](https://developers.google.com/earth-engine/tutorials/community/satellite-embedding-02-unsupervised-classification)

* [Supervised Classification with Satellite Embedding Dataset](https://developers.google.com/earth-engine/tutorials/community/satellite-embedding-03-supervised-classification)

* [Regression with Satellite Embedding Dataset](https://developers.google.com/earth-engine/tutorials/community/satellite-embedding-04-regression)

* [Similarity Search with Satellite Embedding Dataset](https://developers.google.com/earth-engine/tutorials/community/satellite-embedding-05-similarity-search)

* **Satellite Embedding V1**: The AlphaEarth Foundations Satellite Embedding dataset is produced by Google and Google DeepMind.

* **Sentinel-2 Level-1C, Level-2A**: Contains Copernicus Sentinel data.

* **FAO GAUL 500m: Global Administrative Unit Layers 2015, Second-Level Administrative Units**: Source of Administrative boundaries: The Global Administrative Unit Layers (GAUL) dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System

(AMIS) projects.

* Brown, C. F., Kazmierski, M. R., Pasquarella, V J., Rucklidge, W. J., Samsikova, M., Zhang, C., Shelhamer, E., Lahera, E., Wiles, O., Ilyushchenko, S., Gorelick, N., Zhang, L. L., Alj, S., Schechter, E., Askay, S., Guinan, O., Moore, R., Boukouvalas, A., & Kohli, P.(2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv preprint arXiv.2507.22291. https://arxiv.org/abs/2507.22291

* Sherrie Wang, George Azzari, David B. Lobell, Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques, Remote Sensing of Environment, Volume 222, 2019, Pages 303-317, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.12.026.

* Houriez, L., Pilarski, S., Vahedi, B., Ahmadalipour, A., Scully, T. H., Aflitto, N., Andre, D., Jaffe, C., Wedner, M., Mazzola, R., Jeffery, J., Messinger, B., McGinley-Smith, S., & Russell, S. (2025, August). Scalable geospatial data generation using AlphaEarth foundations model (arXiv:2508.11739). arXiv. https://arxiv.org/abs/2508.11739

* Cordeiro, M. C. R.; Martinez, J.-M.; Peña-Luque, S. Automatic Water Detection from Multidimensional Hierarchical Clustering for Sentinel-2 Images and a Comparison with Level 2A Processors.Remote Sensing of Environment 2021, 253, 112209. https://doi.org/10.1016/j.rse.2020.112209.

The course material (text, images, presentation, videos) is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).

The code (scripts, Jupyter notebooks etc.) is licensed under the MIT License. For a copy, see https://opensource.org/licenses/MIT

Kindly give appropriate credit to the original author as below:

Copyright © 2025 Ujaval Gandhi [www.spatialthoughts.com](https://spatialthoughts.com)

You can cite the course materials as follows

* Gandhi, Ujaval, 2025. *Satellite Embedding Deep Dive* workshop. Spatial Thoughts. https://courses.spatialthoughts.com/gee-satellite-embedding.html