GeoDevelopment Observatory of Bolivia / Indicators
Satellite embeddings
What every municipality looks like from space, compressed by a deep-learning model into 64 numbers: features for machine learning.
Sixty-four satellite embedding dimensions (A00–A63) for each of Bolivia’s 339 municipalities in 2017, averaged from the annual 10 m Google Satellite Embedding dataset (AlphaEarth Foundations). The model summarises Sentinel-2, Landsat and other imagery into abstract features that capture land cover, texture, roads and buildings. Two versions are published: emb, the area-weighted mean over each municipality, and embpw, weighted by population so that it describes where people live. The dimensions are not interpretable one by one; they are inputs to models, for example to predict poverty.
Variables of emb
A00: Embedding A00 (area-weighted). Dimension 0 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A01: Embedding A01 (area-weighted). Dimension 1 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A02: Embedding A02 (area-weighted). Dimension 2 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A03: Embedding A03 (area-weighted). Dimension 3 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A04: Embedding A04 (area-weighted). Dimension 4 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A05: Embedding A05 (area-weighted). Dimension 5 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A06: Embedding A06 (area-weighted). Dimension 6 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A07: Embedding A07 (area-weighted). Dimension 7 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A08: Embedding A08 (area-weighted). Dimension 8 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A09: Embedding A09 (area-weighted). Dimension 9 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A10: Embedding A10 (area-weighted). Dimension 10 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A11: Embedding A11 (area-weighted). Dimension 11 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A12: Embedding A12 (area-weighted). Dimension 12 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A13: Embedding A13 (area-weighted). Dimension 13 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A14: Embedding A14 (area-weighted). Dimension 14 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A15: Embedding A15 (area-weighted). Dimension 15 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A16: Embedding A16 (area-weighted). Dimension 16 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A17: Embedding A17 (area-weighted). Dimension 17 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A18: Embedding A18 (area-weighted). Dimension 18 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A19: Embedding A19 (area-weighted). Dimension 19 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A20: Embedding A20 (area-weighted). Dimension 20 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A21: Embedding A21 (area-weighted). Dimension 21 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A22: Embedding A22 (area-weighted). Dimension 22 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A23: Embedding A23 (area-weighted). Dimension 23 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A24: Embedding A24 (area-weighted). Dimension 24 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A25: Embedding A25 (area-weighted). Dimension 25 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A26: Embedding A26 (area-weighted). Dimension 26 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A27: Embedding A27 (area-weighted). Dimension 27 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A28: Embedding A28 (area-weighted). Dimension 28 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A29: Embedding A29 (area-weighted). Dimension 29 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A30: Embedding A30 (area-weighted). Dimension 30 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A31: Embedding A31 (area-weighted). Dimension 31 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A32: Embedding A32 (area-weighted). Dimension 32 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A33: Embedding A33 (area-weighted). Dimension 33 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A34: Embedding A34 (area-weighted). Dimension 34 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A35: Embedding A35 (area-weighted). Dimension 35 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A36: Embedding A36 (area-weighted). Dimension 36 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A37: Embedding A37 (area-weighted). Dimension 37 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A38: Embedding A38 (area-weighted). Dimension 38 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A39: Embedding A39 (area-weighted). Dimension 39 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A40: Embedding A40 (area-weighted). Dimension 40 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A41: Embedding A41 (area-weighted). Dimension 41 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A42: Embedding A42 (area-weighted). Dimension 42 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A43: Embedding A43 (area-weighted). Dimension 43 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A44: Embedding A44 (area-weighted). Dimension 44 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A45: Embedding A45 (area-weighted). Dimension 45 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A46: Embedding A46 (area-weighted). Dimension 46 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A47: Embedding A47 (area-weighted). Dimension 47 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A48: Embedding A48 (area-weighted). Dimension 48 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A49: Embedding A49 (area-weighted). Dimension 49 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A50: Embedding A50 (area-weighted). Dimension 50 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A51: Embedding A51 (area-weighted). Dimension 51 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A52: Embedding A52 (area-weighted). Dimension 52 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A53: Embedding A53 (area-weighted). Dimension 53 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A54: Embedding A54 (area-weighted). Dimension 54 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A55: Embedding A55 (area-weighted). Dimension 55 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A56: Embedding A56 (area-weighted). Dimension 56 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A57: Embedding A57 (area-weighted). Dimension 57 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A58: Embedding A58 (area-weighted). Dimension 58 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A59: Embedding A59 (area-weighted). Dimension 59 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A60: Embedding A60 (area-weighted). Dimension 60 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A61: Embedding A61 (area-weighted). Dimension 61 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A62: Embedding A62 (area-weighted). Dimension 62 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).A63: Embedding A63 (area-weighted). Dimension 63 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with Earth Engine's mean reducer at 10 m, which weights each pixel by its area of overlap. An abstract feature without a physical unit: together the 64 dimensions summarise the year's multi-sensor appearance of the land surface (Sentinel-2, Landsat, radar and other inputs).
Sources
- Brown, C. F., Kazmierski, M. R., Pasquarella, V. J., Rucklidge, W. J., Samsikova, M., Zhang, C., et al. (2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv:2507.22291. https://arxiv.org/abs/2507.22291
- Runfola, D., Anderson, A., Baier, H., et al. (2020). geoBoundaries: A global database of political administrative boundaries. PLoS ONE, 15(4), e0231866. https://doi.org/10.1371/journal.pone.0231866
- Mendez, C., Gonzales, E., Leoni, P., Andersen, L., & Peralta, H. (2026). DS4Bolivia: A Data Science Repository to Study GeoSpatial Development in Bolivia [Data set]. GitHub. https://github.com/quarcs-lab/ds4bolivia
Variables of embpw
A00: Embedding A00 (population-weighted). Dimension 0 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A01: Embedding A01 (population-weighted). Dimension 1 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A02: Embedding A02 (population-weighted). Dimension 2 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A03: Embedding A03 (population-weighted). Dimension 3 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A04: Embedding A04 (population-weighted). Dimension 4 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A05: Embedding A05 (population-weighted). Dimension 5 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A06: Embedding A06 (population-weighted). Dimension 6 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A07: Embedding A07 (population-weighted). Dimension 7 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A08: Embedding A08 (population-weighted). Dimension 8 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A09: Embedding A09 (population-weighted). Dimension 9 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A10: Embedding A10 (population-weighted). Dimension 10 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A11: Embedding A11 (population-weighted). Dimension 11 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A12: Embedding A12 (population-weighted). Dimension 12 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A13: Embedding A13 (population-weighted). Dimension 13 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A14: Embedding A14 (population-weighted). Dimension 14 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A15: Embedding A15 (population-weighted). Dimension 15 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A16: Embedding A16 (population-weighted). Dimension 16 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A17: Embedding A17 (population-weighted). Dimension 17 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A18: Embedding A18 (population-weighted). Dimension 18 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A19: Embedding A19 (population-weighted). Dimension 19 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A20: Embedding A20 (population-weighted). Dimension 20 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A21: Embedding A21 (population-weighted). Dimension 21 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A22: Embedding A22 (population-weighted). Dimension 22 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A23: Embedding A23 (population-weighted). Dimension 23 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A24: Embedding A24 (population-weighted). Dimension 24 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A25: Embedding A25 (population-weighted). Dimension 25 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A26: Embedding A26 (population-weighted). Dimension 26 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A27: Embedding A27 (population-weighted). Dimension 27 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A28: Embedding A28 (population-weighted). Dimension 28 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A29: Embedding A29 (population-weighted). Dimension 29 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A30: Embedding A30 (population-weighted). Dimension 30 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A31: Embedding A31 (population-weighted). Dimension 31 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A32: Embedding A32 (population-weighted). Dimension 32 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A33: Embedding A33 (population-weighted). Dimension 33 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A34: Embedding A34 (population-weighted). Dimension 34 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A35: Embedding A35 (population-weighted). Dimension 35 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A36: Embedding A36 (population-weighted). Dimension 36 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A37: Embedding A37 (population-weighted). Dimension 37 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A38: Embedding A38 (population-weighted). Dimension 38 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A39: Embedding A39 (population-weighted). Dimension 39 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A40: Embedding A40 (population-weighted). Dimension 40 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A41: Embedding A41 (population-weighted). Dimension 41 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A42: Embedding A42 (population-weighted). Dimension 42 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A43: Embedding A43 (population-weighted). Dimension 43 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A44: Embedding A44 (population-weighted). Dimension 44 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A45: Embedding A45 (population-weighted). Dimension 45 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A46: Embedding A46 (population-weighted). Dimension 46 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A47: Embedding A47 (population-weighted). Dimension 47 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A48: Embedding A48 (population-weighted). Dimension 48 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A49: Embedding A49 (population-weighted). Dimension 49 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A50: Embedding A50 (population-weighted). Dimension 50 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A51: Embedding A51 (population-weighted). Dimension 51 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A52: Embedding A52 (population-weighted). Dimension 52 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A53: Embedding A53 (population-weighted). Dimension 53 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A54: Embedding A54 (population-weighted). Dimension 54 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A55: Embedding A55 (population-weighted). Dimension 55 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A56: Embedding A56 (population-weighted). Dimension 56 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A57: Embedding A57 (population-weighted). Dimension 57 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A58: Embedding A58 (population-weighted). Dimension 58 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A59: Embedding A59 (population-weighted). Dimension 59 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A60: Embedding A60 (population-weighted). Dimension 60 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A61: Embedding A61 (population-weighted). Dimension 61 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A62: Embedding A62 (population-weighted). Dimension 62 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.A63: Embedding A63 (population-weighted). Dimension 63 of the 64-dimensional Google Satellite Embedding V1 (AlphaEarth Foundations), annual 10 m embeddings for 2017, averaged over the municipality with each 10 m pixel weighted by its population (WorldPop 100 m, 2017, resampled to 10 m): Σ(embedding × population) / Σ population. It describes the places where people live. An abstract feature without a physical unit.
Sources
- Brown, C. F., Kazmierski, M. R., Pasquarella, V. J., Rucklidge, W. J., Samsikova, M., Zhang, C., et al. (2025). AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data. arXiv:2507.22291. https://arxiv.org/abs/2507.22291
- WorldPop & CIESIN (2018). Global High Resolution Population Denominators Project (100 m, 2000–2020). University of Southampton and Columbia University. Funded by the Bill and Melinda Gates Foundation (OPP1134076). https://doi.org/10.5258/SOTON/WP00645
- Runfola, D., Anderson, A., Baier, H., et al. (2020). geoBoundaries: A global database of political administrative boundaries. PLoS ONE, 15(4), e0231866. https://doi.org/10.1371/journal.pone.0231866
- Mendez, C., Gonzales, E., Leoni, P., Andersen, L., & Peralta, H. (2026). DS4Bolivia: A Data Science Repository to Study GeoSpatial Development in Bolivia [Data set]. GitHub. https://github.com/quarcs-lab/ds4bolivia