isaaccorley/geospatial-skills/tessera

Name: tessera
Author: isaaccorley

Claude Code and agent skill by isaaccorley/geospatial-skills

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Use when working with TESSERA satellite embeddings — downloading via CLI, sampling via the Python or R library, choosing between point-based and mosaic approaches, or exporting to GeoTIFF/NPY/Zarr.

Install

npx vskill@latest install isaaccorley/geospatial-skills/tessera

SKILL.md

Tessera Embeddings Skill

Work with TESSERA satellite embeddings via the geotessera CLI, the Python library, or the R library.

What this skill is for

Use this skill when the user needs to:

check where TESSERA embeddings exist
download embeddings for a bbox or region file
sample embeddings at point locations (Python/R library)
build mosaics for dense raster analysis (Python library)
choose between GeoTIFF, NPY, and Zarr output
configure registry and cache behavior
preview or serve downloaded outputs locally

Install

bash

pip install geotessera                              # Python library + CLI

remotes::install_github("lassa-sentinel/GeoTessera") # R library

Key concepts

embeddings are 128-channel dense vectors at 10m resolution per pixel
tiles are 0.1° × 0.1° (~11km × 11km), stored as quantized int8 + float32 scales
the Parquet registry (~few MB) is cached locally; tiles are fetched on demand
all high-level API methods return dequantized float32 arrays
hash verification is enabled by default

Points vs mosaics — choose the right approach

Prefer `sample_embeddings_at_points()` for most tasks. It only downloads the tiles it needs and returns a compact (N, 128) array. Use it for labeled site extraction, validation, sparse classification, and any workflow where you have specific coordinates.

Use `fetch_mosaic_for_region()` only when you need dense per-pixel coverage — e.g. wall-to-wall land cover classification or spatial clustering over a contiguous area. Mosaics merge and reproject all tiles in a bbox into a single large array, which is memory-intensive for large regions.

Have specific point locations?
  YES → sample_embeddings_at_points()
  NO  → Need wall-to-wall raster output?
          YES → fetch_mosaic_for_region()
          NO  → CLI download + export

Python library

python

from geotessera import GeoTessera
gt = GeoTessera()  # uses default registry and current directory
# --- Point sampling (preferred for most tasks) ---
points = [(0.15, 52.05), (0.25, 52.15), (-1.3, 51.75)]
embeddings = gt.sample_embeddings_at_points(points, year=2024)
# Returns: (N, 128) float32 array, NaN for missing points
# With metadata (tile info, pixel coordinates, CRS)
embeddings, metadata = gt.sample_embeddings_at_points(
    points, year=2024, include_metadata=True
)
# --- Mosaic (dense raster analysis only) ---
bbox = (-0.2, 51.4, 0.1, 51.6)
mosaic, transform, crs = gt.fetch_mosaic_for_region(bbox, year=2024)
# Returns: (H, W, 128) float32 array
# --- Single tile ---
embedding, crs, transform = gt.fetch_embedding(lon=0.15, lat=52.05, year=2024)
# Returns: (H, W, 128) float32, CRS, Affine transform
# --- Export ---
gt.export_embedding_geotiff(lon=0.15, lat=52.05, output_path="tile.tif", year=2024)
gt.export_embedding_zarr(lon=0.15, lat=52.05, output_path="tile.zarr", year=2024)
# --- Registry queries ---
gt.registry.get_available_years()           # [2017, 2018, ..., 2025]
gt.registry.get_tile_counts_by_year()       # {2024: 1234567, ...}
tiles = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)

Point inputs accept list of (lon, lat) tuples, GeoJSON FeatureCollections, or GeoPandas GeoDataFrames.

R library

An R port by Simon Frost (Microsoft Research). Uses R6 classes, sf, terra, and arrow.

remotes::install_github("lassa-sentinel/GeoTessera")
library(GeoTessera)
 
gt <- geotessera()
tiles <- gt$get_tiles(bbox = c(-0.2, 51.4, 0.1, 51.6), year = 2024)
gt$export_embedding_geotiffs(tiles = tiles, output_dir = "london_tiles")

Full R docs: https://lassa-sentinel.github.io/GeoTessera

CLI workflow

1. Check coverage first

bash

geotessera coverage --output coverage_map.png
geotessera coverage --year 2024 --country uk

2. Download for a region

bash

geotessera download --bbox "-0.2,51.4,0.1,51.6" --year 2024 --output ./tiffs
geotessera download --region-file cambridge.geojson --year 2024 --output ./tiffs

3. Pick output format

bash

geotessera download --bbox "..." --format tiff --year 2024 --output ./tiffs  # GIS-ready
geotessera download --bbox "..." --format npy  --year 2024 --output ./arrays # ML-friendly
geotessera download --bbox "..." --format zarr --year 2024 --output ./zarr   # cloud-native

4. Select bands if needed

bash

geotessera download --bbox "..." --bands "0,1,2" --year 2024 --output ./subset

5. Visualize or serve

bash

geotessera visualize ./tiffs --type web --output ./web
geotessera serve ./web --open

Tips

prefer sample_embeddings_at_points() over mosaics for sparse locations
prefer coverage before large CLI downloads
use --format tiff for GIS-ready outputs, --format npy for array-first ML
use --bands to reduce output size when only a few channels are needed
keep --year explicit — data is available from 2017 to 2025
use a fixed registry path or URL for reproducibility

Common mistakes

Mistake	Fix
Building a mosaic just to extract a few points	Use `sample_embeddings_at_points()` instead
Downloading all 128 bands when only a few are needed	Use `--bands` (CLI) or the `bands` parameter
Not checking coverage before a large download	Run `geotessera coverage` first
Forgetting `--year` and getting default year	Always specify `--year` explicitly

References

references/geotessera-cli.md — full CLI flag reference
references/geotessera-library.md — Python library API reference

Security

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Source

SKILL.md in the repositoryCheckingskills/tessera/SKILL.md

Popularity

Repository stars: 57
Repository forks: 8
7-day trend score: 20

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 22 May 2026, 5 months ago.

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