Python library for accessing and working with Tessera geospatial foundation model embeddings.

🚀 TESSERA v2 is here

The TESSERA v2 code is now live — model weights and inference code are available in the ucam-eo/tessera repository. v2 is our next-generation pixel-wise Earth foundation model; see the preprint, TESSERA v2: Scaling Pixel-wise Earth Foundation Models.

Want to try v2 embeddings early? You can pre-request v2 embeddings for your region and become an early tester:

👉 Submit a v2 Embedding Pre-Request

⚠️ Heads-up: we are still ramping up the compute, storage, and release infrastructure for v2, so v2 embeddings will be produced slowly at first and there is no guaranteed turnaround time. If you need embeddings soon, request v1.1 instead — it is fully available today. Scroll down to Request missing embeddings to open a standard v1.1 request.

Overview

GeoTessera provides access to geospatial embeddings from the Tessera foundation model, which processes Sentinel-1 and Sentinel-2 satellite imagery to generate 128-channel representation maps at 10m resolution. These embeddings compress a full year of temporal-spectral features into dense representations optimized for downstream geospatial analysis tasks. Read more details about the model.

Request missing embeddings

This repo provides precomputed embeddings for multiple years and regions. Embeddings are generated by randomly sampling tiles within each region to ensure broad spatial coverage.

If some years (2017–2025) / areas are still missing for your use case, please submit an Embedding Request:

👉 Open an Embedding Request
Please include: your organization, intended use, ROI as a bounding box with four points (lon,lat, 4 decimals), and the year(s).

After you submit the request, we will prioritize your ROI and notify you via a comment in the issue once the embeddings are ready.

Important Notice ⚠️

On 20th August 2025, we updated the data processing pipeline of GeoTessera to resolve the issue of tiling artifacts, as shown below. We have retained the embeddings generated before August 20, as they remain effective for use in small-scale areas. After the 2024 embedding generation is completed, we will reprocess the tiles affected by tiling artifacts. If you observe such artifacts during use and they significantly impact performance, please raise the issue here, and we will prioritize reprocessing your request.

Please note that if the artifacts you observe are slanted, this is not a bug in the pipeline but rather a result of the Sentinel-1/2 satellite trajectories. Currently, Tessera cannot completely eliminate such artifacts, as they reflect the inherent characteristics of the raw data. However, we have observed that they have minimal impact on downstream tasks.

Installation

Requires Python 3.12 or later.

pip install geotessera

For development:

git clone https://github.com/ucam-eo/geotessera
cd geotessera
pip install -e .

Architecture

Core Concepts

GeoTessera is built around a simple two-step workflow:

Retrieve embeddings: Fetch raw numpy arrays for a geographic bounding box
Export to desired format: Save as raw numpy arrays or convert to georeferenced GeoTIFF files

Coordinate System and Tile Grid

The Tessera embeddings use a 0.1-degree grid system:

Tile size: Each tile covers 0.1° × 0.1° (approximately 11km × 11km at the equator)
Tile naming: Tiles are named by their center coordinates (e.g., grid_0.15_52.05)
Tile bounds: A tile at center (lon, lat) covers:
- Longitude: [lon - 0.05°, lon + 0.05°]
- Latitude: [lat - 0.05°, lat + 0.05°]
Resolution: 10m per pixel (variable number of pixels per tile depending on latitude)

File Structure and Downloads

When you request embeddings, GeoTessera downloads files over HTTPS from the public Source Cooperative repository into the output directory you specify, where they persist for re-use:

Embedding Files (via `fetch_embedding`)

Quantized embeddings (grid_X.XX_Y.YY.npy):
- Shape: (height, width, 128)
- Data type: int8 (quantized for storage efficiency)
- Contains the compressed embedding values
Scale files (grid_X.XX_Y.YY_scales.npy):
- Shape: (height, width) or (height, width, 128)
- Data type: float32
- Contains scale factors for dequantization
Dequantization: final_embedding = quantized_embedding * scales
Persistent Storage: Files are downloaded into your chosen output directory and skipped on rerun, so interrupted downloads resume cleanly

Landmask Files (for GeoTIFF export)

When exporting to GeoTIFF, additional landmask files are fetched:

Landmask tiles (grid_X.XX_Y.YY.tiff):
- Provide UTM projection information
- Define precise geospatial transforms
- Contain land/water masks
- Cached alongside the embedding tiles for re-use

Data Flow

User Request (lat/lon bbox)
    ↓
Parquet Registry Lookup (find available tiles from manifest.parquet)
    ↓
HTTPS Downloads from Source Cooperative to Output Directory (integrity verified)
    ├── embedding.npy (quantized) → output dir
    └── embedding_scales.npy → output dir
    ↓
Dequantization (multiply arrays)
    ↓
Output Format
    ├── NumPy arrays → Direct analysis
    └── GeoTIFF → GIS integration

Storage Note: Only the Parquet manifests (tens to a couple of hundred MB per dataset) are cached under ~/.cache/geotessera. Embedding tiles are downloaded on demand into the output directory you specify and persist there for re-use across runs.

Quick Start

Check Available Data

Before downloading, check what data is available:

# Generate a coverage map showing all available tiles
geotessera coverage --output coverage_map.png
# Generate a coverage map for the UK
geotessera coverage --country uk
# View coverage for a specific year
geotessera coverage --year 2024 --output coverage_2024.png
# Customize the visualization
geotessera coverage --year 2024 --tile-color blue --tile-alpha 0.3

Download Embeddings

Download embeddings as either numpy arrays or GeoTIFF files:

# Download as GeoTIFF (default, with georeferencing)
geotessera download \
  --bbox "-0.2,51.4,0.1,51.6" \
  --year 2024 \
  --output ./london_tiffs
# Download as raw numpy arrays (with metadata JSON)
geotessera download \
  --bbox "-0.2,51.4,0.1,51.6" \
  --format npy \
  --year 2024 \
  --output ./london_arrays
# Download using a GeoJSON/Shapefile region
geotessera download \
  --region-file cambridge.geojson \
  --format tiff \
  --year 2024 \
  --output ./cambridge_tiles
# Download specific bands only
geotessera download \
  --bbox "-0.2,51.4,0.1,51.6" \
  --bands "0,1,2" \
  --year 2024 \
  --output ./london_rgb

Create Visualizations

Generate PCA visualizations and web maps from downloaded GeoTIFFs:

# Create a PCA mosaic from downloaded tiles
geotessera visualize ./london_tiffs pca_mosaic.tif
# Use histogram equalization for maximum contrast
geotessera visualize ./london_tiffs pca_balanced.tif --balance histogram
# Create web tiles and serve interactively
geotessera webmap pca_mosaic.tif --serve
# Serve existing web visualizations locally
geotessera serve ./london_web --open

Python API

Core Methods

The library provides two main methods for retrieving embeddings:

from geotessera import GeoTessera
# Initialize the client
gt = GeoTessera()
# Method 1: Fetch a single tile
embedding, crs, transform = gt.fetch_embedding(lon=0.15, lat=52.05, year=2024)
print(f"Shape: {embedding.shape}")  # e.g., (1200, 1200, 128)
print(f"CRS: {crs}")  # Coordinate reference system from landmask
# Method 2: Fetch all tiles in a bounding box
bbox = (-0.2, 51.4, 0.1, 51.6)  # (min_lon, min_lat, max_lon, max_lat)
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)
embeddings = gt.fetch_embeddings(tiles_to_fetch)
for year, tile_lon, tile_lat, embedding_array, crs, transform in embeddings:
    print(f"Tile ({tile_lat}, {tile_lon}): {embedding_array.shape}")

Export Formats

Export as GeoTIFF

# Export embeddings for a region as individual GeoTIFF files
# Step 1: Get the tiles for the region
bbox = (-0.2, 51.4, 0.1, 51.6)
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)
# Step 2: Export those tiles as GeoTIFFs
files = gt.export_embedding_geotiffs(
    tiles_to_fetch=tiles_to_fetch,
    output_dir="./output",
    bands=None,  # Export all 128 bands (default)
    compress="lzw"  # Compression method
)
print(f"Created {len(files)} GeoTIFF files")
# Export specific bands only (e.g., first 3 for RGB visualization)
files = gt.export_embedding_geotiffs(
    tiles_to_fetch=tiles_to_fetch,
    output_dir="./rgb_output",
    bands=[0, 1, 2]  # Only export first 3 bands
)

Work with NumPy Arrays

# Fetch and process embeddings directly
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)
embeddings = gt.fetch_embeddings(tiles_to_fetch)
for year, tile_lon, tile_lat, embedding, crs, transform in embeddings:
    # Compute statistics
    mean_values = np.mean(embedding, axis=(0, 1))  # Mean per channel
    std_values = np.std(embedding, axis=(0, 1))    # Std per channel
    # Extract specific pixels
    center_pixel = embedding[embedding.shape[0]//2, embedding.shape[1]//2, :]
    # Apply custom processing
    processed = your_analysis_function(embedding)

Visualization Functions

from geotessera.visualization import (
    create_rgb_mosaic,
    visualize_global_coverage
)
from geotessera.web import (
    create_coverage_summary_map,
    geotiff_to_web_tiles
)
# Create an RGB mosaic from multiple GeoTIFF files
create_rgb_mosaic(
    geotiff_paths=["tile1.tif", "tile2.tif"],
    output_path="mosaic.tif",
    bands=(0, 1, 2)  # RGB bands
)
# Generate web tiles for interactive maps
geotiff_to_web_tiles(
    geotiff_path="mosaic.tif",
    output_dir="./web_tiles",
    zoom_levels=(8, 15)
)
# Create a global coverage visualization
visualize_global_coverage(
    tessera_client=gt,
    output_path="global_coverage.png",
    year=2024,  # Or None for all years
    width_pixels=2000,
    tile_color="red",
    tile_alpha=0.6
)

Cloud-Native Zarr Access

Note

Zarr support is forthcoming in a future release. The hosted Zarr store has not yet been published to the new Source Cooperative repository, so the GeoTesseraZarr API below is a preview and will not find data at the default location until the store is uploaded. Use the download workflow above in the meantime.

For interactive or large-scale analysis without downloading files, use the Zarr store. This streams data directly from the cloud:

from geotessera.store import GeoTesseraZarr
gt = GeoTesseraZarr()
print(gt.years)  # [2017, 2018, ..., 2025]
# Sample embeddings at specific points (no download needed)
X = gt.sample_points([(-2.97, 53.44), (0.15, 52.05)], year=2025)
print(f"Shape: {X.shape}")  # (2, 128)
# Read a full region as a mosaic
mosaic, transform, crs = gt.read_region(
    (-3.0, 53.4, -2.9, 53.5), year=2025,
)
print(f"Mosaic shape: {mosaic.shape}")
# Work with individual UTM zones via xarray
ds = gt.open_zone(lon=0.15)
print(ds)

The Zarr store implements the geoemb: convention for geospatial embedding data and automatically routes queries to the correct UTM zone.

CLI Reference

download

Download embeddings for a region in your preferred format:

geotessera download [OPTIONS]
Options:
  -o, --output PATH         Output directory [required]
  --bbox TEXT              Bounding box: 'lon,lat' (single tile) or 'min_lon,min_lat,max_lon,max_lat'
  --tile TEXT              Single tile by any point within it: 'lon,lat'
  --region-file PATH       GeoJSON/Shapefile to define region
  --country TEXT           Country name (e.g., 'United Kingdom', 'UK', 'GB')
  -f, --format TEXT        Output format: 'tiff' or 'npy' (default: tiff)
  --year INT               Year of embeddings (default: 2024)
  --dataset-version TEXT   Tessera dataset version (e.g. v1, v1.1)
  --dataset-variant TEXT   Tessera dataset variant (default: the version's default variant; list with `geotessera info`)
  --bands TEXT             Comma-separated band indices (default: all 128)
  --compress TEXT          Compression for TIFF format (default: lzw)
  --dry-run                Calculate total download size without downloading
  --list-files             List all created files with details
  -v, --verbose            Verbose output

Resume behaviour: Both TIFF and NPY downloads automatically skip files that already exist on disk, so interrupted downloads can be resumed by re-running the same command.

Single tile examples:

# Download a single tile containing a specific point
geotessera download --tile "0.17,52.23" --year 2024 -o ./single_tile
# Same result using --bbox with 2 coordinates
geotessera download --bbox "0.17,52.23" --year 2024 -o ./single_tile

Output formats:

tiff: Georeferenced GeoTIFF files with UTM projection
npy: Raw numpy arrays with metadata.json file

visualize

Create PCA visualization from multiband GeoTIFF or NPY format embeddings:

geotessera visualize INPUT_PATH OUTPUT_FILE [OPTIONS]
Options:
  --n-components INT       Number of PCA components (default: 3)
  --crs TEXT               Target CRS for reprojection (default: EPSG:3857)
  --balance TEXT            RGB balance method: histogram, percentile, or adaptive
  --percentile-low FLOAT   Lower percentile for percentile balance (default: 2.0)
  --percentile-high FLOAT  Upper percentile for percentile balance (default: 98.0)

webmap

Create web tiles and interactive viewer from a PCA mosaic:

geotessera webmap RGB_MOSAIC [OPTIONS]
Options:
  -o, --output PATH        Output directory
  --min-zoom INT           Min zoom for web tiles (default: 8)
  --max-zoom INT           Max zoom for web tiles (default: 15)
  --serve/--no-serve       Start web server immediately
  -p, --port INT           Port for web server (default: 8000)
  --region-file PATH       GeoJSON/Shapefile boundary to overlay
  --force/--no-force       Force regeneration of tiles

coverage

Generate a world map showing data availability:

geotessera coverage [OPTIONS]
Options:
  -o, --output PATH        Output PNG file, or a directory to also receive the
                           coverage.json/globe.html (default: tessera_coverage.png)
  --year INT               Specific year to visualize
  --bbox TEXT              Bounding box: 'lon,lat' (single tile) or 'min_lon,min_lat,max_lon,max_lat'
  --tile TEXT              Single tile by any point within it: 'lon,lat'
  --by-source              Render each (version, variant) source in a distinct colour
  --dataset-version TEXT   Tessera dataset version (e.g. v1, v1.1; or 'all' with --by-source)
  --dataset-variant TEXT   Tessera dataset variant (default: the version's default variant; or 'all' with --by-source)
  --region-file PATH       GeoJSON/Shapefile to focus on specific region
  --country TEXT           Country name to focus on (e.g., 'United Kingdom')
  --tile-color TEXT        Color for tiles (default: red)
  --tile-alpha FLOAT       Transparency 0-1 (default: 0.6)
  --tile-size FLOAT        Size multiplier (default: 1.0)
  --width INT              Output image width in pixels (default: 2000)
  --no-countries           Don't show country boundaries
  --no-multi-year-colors   Disable multi-year color coding

serve

Serve web visualizations locally:

geotessera serve DIRECTORY [OPTIONS]
Options:
  -p, --port INT           Port number (default: 8000)
  --open/--no-open         Auto-open browser (default: open)
  --html TEXT              Specific HTML file to serve

info

Display information about GeoTIFF files or the library:

geotessera info [OPTIONS]
Options:
  --tiles PATH             Analyze tile files/directory (GeoTIFF or NPY format)
  --dataset-version TEXT   Tessera dataset version (e.g. v1, v1.1)
  --dataset-variant TEXT   Tessera dataset variant (default: the version's default variant; list with `geotessera info`)
  -v, --verbose            Verbose output

Registry System

Overview

GeoTessera uses a Parquet-based registry system to efficiently manage and access the large Tessera dataset:

Per-version manifests: Each dataset version has its own manifest.parquet listing every (year, lon, lat) tile available for that version's variants
Fast queries: Uses pandas DataFrames for efficient spatial and temporal filtering
Block-based organization: Internal 5×5 degree geographic blocks for efficient queries
Minimal storage: Only manifest files (tens to a couple of hundred MB per dataset) are cached locally
Integrity checking: Every download is verified against the response Content-Length, and against an MD5 computed over the streamed body whenever the server's ETag is a content MD5 (single-part uploads)
- A mismatch rejects the download and triggers a retry, so corrupt or truncated files never reach the cache

Dataset Versions and Variants

Tessera embeddings are published as dataset versions (e.g. v1, v1.1, v2) and, within a version, as variants produced by different model runs. Each (version, variant) pair — a dataset — has its own directory in the repository's npy/ tree:

Version	Variant	`npy/` directory	Status
`1.0`	`vultr` (default)	`v1/`	available
`1.1`	`cambridge` (default)	`v1.1-cam/`	available
`1.1`	`dclimate`	—	coming soon
`2.0`	`2B-L~beta1` (default)	`v2-2B-L~beta1/`	available

The v1 series predates the variant-suffix scheme, so all its variants share the bare v1/ directory. The library defaults remain dataset_version="v1" and year=2024 — the only combination with full global coverage today. List the datasets at any time with geotessera info, and select them on the CLI with --dataset-version and --dataset-variant, or in Python:

gt = GeoTessera(dataset_version="v1.1", dataset_variant="cambridge")

Use geotessera coverage --by-source to render each (version, variant) source in a distinct colour on the coverage map and globe viewer.

Registry Sources

The registry can be loaded from multiple sources (in priority order):

Local file (via registry_path parameter)
Local directory (via --registry-dir or registry_dir parameter, looks for manifest.parquet, falling back to the legacy registry.parquet)
Remote URL (via registry_url parameter)
Default remote (from https://data.source.coop/tessera/tessera/npy/{dataset}/manifest.parquet, where {dataset} encodes the (version, variant) pair: v1, v1.1-cam, v2-2B-L~beta1)

# Use local manifest file
gt = GeoTessera(registry_path="/path/to/manifest.parquet")
# Use local registry directory
gt = GeoTessera(registry_dir="/path/to/registry-dir")
# Use default remote manifest (downloads and caches automatically)
gt = GeoTessera()  # Default behavior

Registry Structure

The Parquet manifest contains columns for:

Coordinates: lon, lat (tile center coordinates)
Year: year (data year, 2017-2025)
Size: file_size (file size in bytes for download planning)

# Example manifest query
import pandas as pd
manifest = pd.read_parquet("manifest.parquet")
print(manifest.head())

Regenerating Manifests (Maintainers)

The per-version manifests can be rebuilt at any time by scanning the public Source Cooperative repository itself — no local copy of the data is needed:

# Rescan every dataset and write one manifest per npy/ directory
# (./manifests/{v1,v1.1-cam,v2-2B-L~beta1}/manifest.parquet) plus a
# per-version landmasks.parquet (./manifests/{v1,v1.1,v2}/landmasks.parquet)
geotessera-registry s3scan s3://tessera/tessera/npy/ \
    --landmasks-uri s3://tessera/tessera/landmasks/ \
    --output ./manifests
# Or scope to a single dataset — the directory name encodes the
# (version, variant) pair, so no --variant flag is needed
geotessera-registry s3scan "s3://tessera/tessera/npy/v1.1-cam/" \
    --landmasks-uri s3://tessera/tessera/landmasks/v1.1/ \
    --output ./manifests

The scan uses anonymous S3 ListObjectsV2 calls against https://data.source.coop, so no credentials are required to regenerate. Uploading the results does require source.coop write access, and the two parquet files go to different trees (matching where clients fetch them — note the npy/ tree is keyed by dataset directory, the landmasks/ tree by plain version):

aws s3 cp manifests/v1.1-cam/manifest.parquet \
    s3://tessera/tessera/npy/v1.1-cam/manifest.parquet \
    --endpoint-url https://data.source.coop
aws s3 cp manifests/v1.1/landmasks.parquet \
    s3://tessera/tessera/landmasks/v1.1/landmasks.parquet \
    --endpoint-url https://data.source.coop

The s3scan summary panel prints these per-file upload commands for you. Transient listing failures (Cloudflare 503s, timeouts) are retried with exponential backoff; if a shard still fails after retries, the affected manifest is not written and the command exits non-zero so an incomplete manifest can never be uploaded. See the maintenance guide for the full workflow, including caching caveats.

How Registry Loading Works

Load Parquet manifest → Download and cache the dataset's manifest (if not local)
Request tiles for bbox → Query DataFrame for tiles in region
Filter by year and variant → Select tiles matching the requested year/variant
Find available tiles → Return list of matching tiles
HTTPS download → Fetch tiles on demand from the Source Cooperative mirror into the output directory, with integrity checks
Persist → Downloaded tiles stay in the output directory and are skipped on rerun

Data Organization

Tessera Data Structure

Remote Server (https://data.source.coop/tessera/tessera)
├── npy/                                       # NPY embeddings + scales
│   │                                          # (one dir per (version, variant) dataset)
│   ├── v1/                                    # 1.0 — all variants share this dir
│   │   ├── manifest.parquet                   # Per-dataset tile manifest
│   │   └── 2024/grid_0.15_52.05/grid_0.15_52.05{,_scales}.npy
│   ├── v1.1-cam/                              # 1.1 / cambridge
│   │   ├── manifest.parquet
│   │   └── 2024/grid_0.15_52.05/grid_0.15_52.05{,_scales}.npy
│   └── v2-2B-L~beta1/                         # 2.0 / 2B-L~beta1 (beta)
│       └── 2024/grid_0.15_52.05/grid_0.15_52.05{,_scales}.npy
├── landmasks/                                 # Landmask TIFFs (per version)
│   ├── v1/
│   │   ├── landmasks.parquet                  # Landmask manifest
│   │   └── grid_0.15_52.05.tiff               # Landmask with projection info
│   ├── v1.1/
│   │   ├── landmasks.parquet
│   │   └── grid_0.15_52.05.tiff
│   └── v2/
│       ├── landmasks.parquet
│       └── grid_0.15_52.05.tiff
└── zarr/                                      # Cloud-native zarr store
    └── v1/                                    # (forthcoming in a future release)

Local Cache Structure

~/.cache/geotessera/                 # Default cache location (manifests only)
├── v1/                              # 1.0 dataset dir + v1 landmask registry
│   ├── manifest.parquet
│   └── landmasks.parquet
├── v1.1-cam/                        # 1.1/cambridge dataset dir
│   └── manifest.parquet
├── v1.1/                            # v1.1 landmask registry
│   └── landmasks.parquet
├── v2-2B-L~beta1/                   # 2.0 beta dataset dir
│   └── manifest.parquet
└── v2/                              # v2 landmask registry
    └── landmasks.parquet
# Note: Embedding and landmask tiles are NOT stored here. They are downloaded
# into the output directory you specify and persist there for re-use.

Coordinate Reference Systems

Embeddings: Stored in simple arrays, referenced by center coordinates
GeoTIFF exports: Use UTM projection from corresponding landmask tiles
Web visualizations: Reprojected to Web Mercator (EPSG:3857)

Cache Configuration

GeoTessera caches only the per-dataset Parquet manifests (tens to a couple of hundred MB each). Embedding and landmask tiles are downloaded into the output directory you specify and persist there for re-use across runs.

Python API

from geotessera import GeoTessera
# Use custom cache directory for registry
gt = GeoTessera(cache_dir="/path/to/cache")
# Use default cache location (recommended)
gt = GeoTessera()

CLI

# Specify custom cache directory
geotessera download --cache-dir /path/to/cache ...
# Use default cache location
geotessera download ...

Default Cache Locations

When cache_dir is not specified, the registry is cached in platform-appropriate locations:

Linux/macOS: $XDG_CACHE_HOME/geotessera or ~/.cache/geotessera
Windows: %LOCALAPPDATA%/geotessera

Hash Verification

GeoTessera verifies every downloaded file (embeddings, scales, and landmasks) against the response Content-Length, and additionally against an MD5 computed over the streamed body whenever the server's ETag is a content MD5 (a single-part upload; this covers landmask TIFFs and scales files). Large multipart-uploaded embedding tiles carry a composite ETag that is not a content hash, so they are length-checked only. A mismatch rejects the download and triggers a retry with backoff, so corrupt or truncated files never reach the cache.

Contributing

Contributions are welcome! Please see our Contributing Guide for details. This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use Tessera in your research, please cite the arXiv paper:

@misc{feng2025tesseratemporalembeddingssurface,
      title={TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis},
      author={Zhengpeng Feng and Clement Atzberger and Sadiq Jaffer and Jovana Knezevic and Silja Sormunen and Robin Young and Madeline C Lisaius and Markus Immitzer and David A. Coomes and Anil Madhavapeddy and Andrew Blake and Srinivasan Keshav},
      year={2025},
      eprint={2506.20380},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2506.20380},
}

🚀 TESSERA v2 is here

Overview

Request missing embeddings

Important Notice ⚠️

Table of Contents

Installation

Architecture

Core Concepts

Coordinate System and Tile Grid

File Structure and Downloads

Embedding Files (via fetch_embedding)

Landmask Files (for GeoTIFF export)

Data Flow

Quick Start

Check Available Data

Download Embeddings

Create Visualizations

Python API

Core Methods

Export Formats

Export as GeoTIFF

Work with NumPy Arrays

Visualization Functions

Cloud-Native Zarr Access

CLI Reference

download

visualize

webmap

coverage

serve

info

Registry System

Overview

Dataset Versions and Variants

Registry Sources

Registry Structure

Regenerating Manifests (Maintainers)

How Registry Loading Works

Data Organization

Tessera Data Structure

Local Cache Structure

Coordinate Reference Systems

Cache Configuration

Python API

CLI

Default Cache Locations

Hash Verification

Contributing

Citation

Links

Star History

Embedding Files (via `fetch_embedding`)