Alphaearth Foundations Change Detection Agent Skill

by google

>- Use AlphaEarth Foundations Satellite Embeddings in Google Earth Engine (GEE) for change detection.

582 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

813

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/google/earthengine-community --skill alphaearth_foundations_change_detection

The instruction itself

2 sections, as written by the author

See introduction in alphaearth_foundations_core.

1. Basic Dot Product (Cosine Similarity) & Visualization

Because the bands in GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL are already unit vectors, their cosine similarity (dot product) is calculated simply by element-wise multiplication followed by a band sum.

To select input, use .first() for point-based queries or .mosaic() for

larger geographic regions.

dataset = ee.ImageCollection('GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL')

# --- OPTION A: For a Point of Interest (POI) ---
point = ee.Geometry.Point([-121.8036, 39.0372])

image1 = dataset.filterDate('2023-01-01', '2024-01-01') \
                .filterBounds(point) \
                .first()

image2 = dataset.filterDate('2024-01-01', '2025-01-01') \
                .filterBounds(point) \
                .first()

# --- OPTION B: For a Large Area of Interest (AOI) ---
# image1 = dataset.filterDate('2023-01-01', '2024-01-01').mosaic()
# image2 = dataset.filterDate('2024-01-01', '2025-01-01').mosaic()

# 1. Qualitative Visualization (Pseudo-RGB parameters)
# Three specific axes of the 64D embedding space provide qualitative surface context.
vis_params = {'min': -0.3, 'max': 0.3, 'bands': ['A01', 'A16', 'A09']}

# 2. Quantitative Similarity (Dot Product)
# Calculates a single-band similarity map from -1.0 to 1.0.
dot_product = image1.multiply(image2).reduce(ee.Reducer.sum())

2. Vectorizing and Filtering Change Polygons

To extract change polygons:

similarity_threshold = 0.8
area_threshold = 20000
scale = 100

# Mask out pixels that did not undergo change (similarity above threshold)
change_mask = dot_product.lt(similarity_threshold).selfMask()

# Convert changed pixels to polygons
# aoi: User-supplied ee.Geometry Area of Interest
change_vectors = change_mask.reduceToVectors(
    reducer=ee.Reducer.countEvery(),
    geometry=aoi,
    scale=scale,
    maxPixels=1e13
)

# Filter by minimum area threshold
filtered_vectors = change_vectors.map(
    lambda feature: ee.Feature(feature).set('area', ee.Feature(feature).geometry().area(1))
).filter(ee.Filter.gt('area', area_threshold))

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How to use it

Copy the folder

Take google/alphaearth_foundations_change_detection from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

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