Tutorials

This section contains comprehensive tutorials for different use cases with GeoTessera.

Tutorial 1: Basic Data Analysis

This tutorial covers the fundamentals of downloading and analyzing Tessera embeddings using Python.

Setup

First, install GeoTessera and import required libraries:

pip install geotessera matplotlib jupyter

# In Python/Jupyter
import numpy as np
import matplotlib.pyplot as plt
from geotessera import GeoTessera
import json

Initialize the Client

Create a GeoTessera client:

# Initialize with default settings
gt = GeoTessera()

# Check available years
years = gt.registry.get_available_years()
print(f"Available years: {years}")

Explore Data Availability

Before downloading, check what data is available using the CLI:

# Generate a coverage map
# CLI command (preferred):
# geotessera coverage --year 2024 --output global_coverage.png

# For specific regions with boundary visualization:
# geotessera coverage --country "United Kingdom" --year 2024  # Shows precise UK boundaries
# geotessera coverage --region-file study_area.geojson --year 2024  # Shows custom region outline

# Or using Python API:
from geotessera.visualization import visualize_global_coverage

visualize_global_coverage(
    tessera_client=gt,
    output_path="global_coverage.png",
    year=2024,
    width_pixels=2000,
    tile_color="blue",
    tile_alpha=0.4
)

print("Coverage map saved to global_coverage.png")
print("Next step: Download data for your region")
print("For better regional focus, use: geotessera coverage --country 'YourCountry'")

Download and Analyze a Single Tile

Start with a single tile for Cambridge, UK:

# Download embedding for Cambridge (note: lon, lat order)
lon, lat = 0.15, 52.05
year = 2024

embedding, crs, transform = gt.fetch_embedding(lon=lon, lat=lat, year=year)

print(f"Embedding shape: {embedding.shape}")
print(f"Data type: {embedding.dtype}")
print(f"CRS: {crs}")
print(f"Transform: {transform}")
print(f"Value range: [{embedding.min():.3f}, {embedding.max():.3f}]")
print(f"Memory usage: {embedding.nbytes / 1024**2:.1f} MB")

Basic Statistical Analysis

Compute basic statistics for the embedding:

# Per-channel statistics
mean_per_channel = np.mean(embedding, axis=(0, 1))
std_per_channel = np.std(embedding, axis=(0, 1))

print("First 10 channels:")
for i in range(10):
    print(f"  Channel {i:2d}: mean={mean_per_channel[i]:6.3f}, "
          f"std={std_per_channel[i]:6.3f}")

# Spatial statistics
center_pixel = embedding[embedding.shape[0]//2, embedding.shape[1]//2, :]
corner_pixel = embedding[0, 0, :]

print(f"\nCenter pixel (first 5 channels): {center_pixel[:5]}")
print(f"Corner pixel (first 5 channels): {corner_pixel[:5]}")

Visualize Individual Channels

Create visualizations of different channels:

fig, axes = plt.subplots(2, 3, figsize=(15, 10))
axes = axes.flatten()

# Visualize channels 0, 10, 20, 30, 60, 100
channels_to_plot = [0, 10, 20, 30, 60, 100]

for i, channel in enumerate(channels_to_plot):
    ax = axes[i]
    im = ax.imshow(embedding[:, :, channel], cmap='viridis')
    ax.set_title(f'Channel {channel}')
    ax.axis('off')
    plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)

plt.tight_layout()
plt.savefig('cambridge_channels.png', dpi=150, bbox_inches='tight')
plt.show()

Multi-Tile Regional Analysis

Download and analyze multiple tiles for a region:

# Define bounding box for Cambridge area
bbox = (0.0, 52.0, 0.3, 52.2)  # (min_lon, min_lat, max_lon, max_lat)

# Step 1: Get list of available tiles in the region
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)
print(f"Found {len(tiles_to_fetch)} tiles in the region")

# Step 2: Fetch all tiles with projection info (returns generator)
tiles = gt.fetch_embeddings(tiles_to_fetch)

# Analyze each tile
tile_stats = []
for year, tile_lon, tile_lat, embedding, crs, transform in tiles:
    stats = {
        'lat': tile_lat,
        'lon': tile_lon,
        'mean_all_channels': np.mean(embedding),
        'std_all_channels': np.std(embedding),
        'channel_50_mean': np.mean(embedding[:, :, 50]),
        'channel_50_std': np.std(embedding[:, :, 50]),
        'crs': str(crs)
    }
    tile_stats.append(stats)

    print(f"Tile ({tile_lon:.2f}, {tile_lat:.2f}): "
          f"overall_mean={stats['mean_all_channels']:.3f}, "
          f"ch50_mean={stats['channel_50_mean']:.3f}")

Save Analysis Results

Save the analysis results for later use:

# Save tile statistics
with open('cambridge_analysis.json', 'w') as f:
    json.dump(tile_stats, f, indent=2)

# Save raw embeddings for further analysis
# Note: Need to re-fetch tiles if already consumed the generator
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)
tiles = gt.fetch_embeddings(tiles_to_fetch)

for year, tile_lon, tile_lat, embedding, crs, transform in tiles:
    filename = f'cambridge_tile_{tile_lat:.2f}_{tile_lon:.2f}.npy'
    np.save(filename, embedding)
    print(f"Saved {filename}")

Tutorial 2: Point Sampling Workflow

This tutorial shows how to efficiently sample embeddings at specific point locations.

Sample Embeddings at Points

The sample_embeddings_at_points() method provides an efficient way to extract embedding values at arbitrary locations:

from geotessera import GeoTessera
import numpy as np

gt = GeoTessera()

# Define points of interest (lon, lat tuples)
points = [
    (0.15, 52.05),   # Cambridge
    (0.25, 52.15),   # Nearby location
    (-0.05, 51.55),  # London
    (-0.12, 51.50),  # Westminster
]

# Sample embeddings at these points (auto-downloads tiles if needed)
embeddings = gt.sample_embeddings_at_points(points, year=2024)
print(f"Sampled embeddings shape: {embeddings.shape}")  # (4, 128)

# Analyze the sampled embeddings
for i, point in enumerate(points):
    print(f"Point {i} ({point[0]}, {point[1]}):")
    print(f"  Mean: {np.mean(embeddings[i]):.3f}")
    print(f"  Std: {np.std(embeddings[i]):.3f}")
    print(f"  First 5 channels: {embeddings[i][:5]}")

Get Metadata About Samples

Include metadata to know which tile and pixel each sample came from:

# Sample with metadata
embeddings, metadata = gt.sample_embeddings_at_points(
    points, year=2024, include_metadata=True
)

for i, meta in enumerate(metadata):
    print(f"Point {i}:")
    print(f"  From tile: ({meta['tile_lon']}, {meta['tile_lat']})")
    print(f"  Pixel location: row={meta['pixel_row']}, col={meta['pixel_col']}")
    print(f"  CRS: {meta['crs']}")

Offline Mode for Point Sampling

For guaranteed offline operation with no network requests:

from geotessera import GeoTessera

# Initialize with embeddings directory containing pre-downloaded tiles
gt = GeoTessera(embeddings_dir="./my_tiles")

# Option 1: Pre-download tiles first
points = [(0.15, 52.05), (0.25, 52.15)]
gt.download_tiles_for_points(points, year=2024)

# Option 2: Sample in offline mode (will fail if tiles are missing)
embeddings = gt.sample_embeddings_at_points(
    points, year=2024, auto_download=False
)

Sample from GeoJSON or GeoDataFrame

You can also pass GeoJSON or GeoPandas GeoDataFrame directly:

import geopandas as gpd

# From GeoJSON FeatureCollection
geojson = {
    "type": "FeatureCollection",
    "features": [
        {"type": "Feature", "geometry": {"type": "Point", "coordinates": [0.15, 52.05]}},
        {"type": "Feature", "geometry": {"type": "Point", "coordinates": [0.25, 52.15]}},
    ]
}
embeddings = gt.sample_embeddings_at_points(geojson, year=2024)

# From GeoPandas GeoDataFrame
gdf = gpd.GeoDataFrame(
    {'name': ['Cambridge', 'Nearby']},
    geometry=gpd.points_from_xy([0.15, 0.25], [52.05, 52.15]),
    crs='EPSG:4326'
)
embeddings = gt.sample_embeddings_at_points(gdf, year=2024)

Tutorial 3: GIS Integration Workflow

This tutorial shows how to work with GeoTIFF exports for GIS software integration.

Check Coverage and Export Embeddings as GeoTIFF

First check coverage, then export as georeferenced GeoTIFF files:

# Step 1: Check coverage (CLI recommended)
# geotessera coverage --bbox "-0.2,51.4,0.1,51.6" --year 2024

from geotessera import GeoTessera

gt = GeoTessera()

# Define region (London area)
bbox = (-0.2, 51.4, 0.1, 51.6)
year = 2024

# Step 2: Download via CLI (preferred) or Python API
# CLI: geotessera download --bbox "-0.2,51.4,0.1,51.6" --year 2024 --output ./london_full
# CLI: geotessera download --bbox "-0.2,51.4,0.1,51.6" --year 2024 --bands "30,60,90" --output ./london_rgb

# Or using Python API:
# Export all bands
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=year)
all_files = gt.export_embedding_geotiffs(
    tiles_to_fetch,
    output_dir="./london_full",
    compress="lzw"
)

print(f"Exported {len(all_files)} GeoTIFF files")
print("Next step: geotessera visualize ./london_full pca_mosaic.tif")

# Export RGB subset for visualization
rgb_files = gt.export_embedding_geotiffs(
    tiles_to_fetch,
    output_dir="./london_rgb",
    bands=[30, 60, 90],  # Custom RGB bands
    compress="lzw"
)

print(f"Exported {len(rgb_files)} RGB GeoTIFF files")
print("Next step: geotessera visualize ./london_rgb pca_rgb_mosaic.tif")

Inspect GeoTIFF Metadata

Check the georeferencing information:

import rasterio

# Inspect the first file
sample_file = all_files[0]

with rasterio.open(sample_file) as src:
    print(f"File: {sample_file}")
    print(f"Shape: {src.shape}")
    print(f"Bands: {src.count}")
    print(f"Data type: {src.dtypes[0]}")
    print(f"CRS: {src.crs}")
    print(f"Transform: {src.transform}")
    print(f"Bounds: {src.bounds}")

    # Read a sample of the data
    sample_data = src.read(1)  # Read first band
    print(f"Data range: [{sample_data.min():.3f}, {sample_data.max():.3f}]")

Create PCA Visualization

Create a PCA visualization from the exported tiles:

# Using CLI:
# geotessera visualize ./london_rgb pca_rgb_mosaic.tif
# geotessera visualize ./london_full pca_full_mosaic.tif --n-components 5

# This creates a PCA-based RGB mosaic that:
# 1. Combines all embedding data across tiles
# 2. Applies PCA transformation for dimensionality reduction
# 3. Maps first 3 principal components to RGB channels
# 4. Eliminates tiling artifacts through consistent PCA across region

print("PCA mosaic created")
print("Next step: geotessera webmap pca_rgb_mosaic.tif --serve")

Generate Web Tiles and Viewer

Create interactive web tiles from the PCA mosaic:

# Using CLI:
# geotessera webmap pca_rgb_mosaic.tif --serve

# This command automatically:
# 1. Reprojects mosaic for web viewing if needed
# 2. Generates web tiles at multiple zoom levels
# 3. Creates HTML viewer with Leaflet map
# 4. Starts web server and opens in browser

# For custom options:
# geotessera webmap pca_rgb_mosaic.tif --min-zoom 6 --max-zoom 18 --output webmap/ --serve

print("Web tiles and viewer created")
print("Interactive map should open in your browser")

QGIS Integration

Tips for using the GeoTIFF files in QGIS:

1. Loading files: Drag and drop GeoTIFF files directly into QGIS

2. Projection: Files use UTM projection - QGIS will handle reprojection automatically

3. Styling: Use single-band pseudocolor for individual channels

4. RGB composites: Use the RGB mosaic files for natural color visualization

5. Analysis: Use QGIS raster calculator for band math operations

Example QGIS workflow:

# In QGIS Python console
from qgis.core import QgsRasterLayer

# Load a GeoTIFF
layer = QgsRasterLayer('/path/to/london_full/grid_51.45_-0.05.tif', 'Tessera Embedding')
QgsProject.instance().addMapLayer(layer)

# Set single-band pseudocolor for channel 50
from qgis.core import QgsColorRampShader, QgsSingleBandPseudoColorRenderer

renderer = QgsSingleBandPseudoColorRenderer(layer.dataProvider(), 50)  # Channel 50
shader = QgsColorRampShader()
# Configure color ramp...
layer.setRenderer(renderer)

Tutorial 4: Large-Scale Analysis

This tutorial covers working with large regions and multiple years of data.

Memory-Efficient Processing

When working with large regions, use CLI for efficient processing:

# Use CLI for large regions
# Step 1: Check coverage first
# geotessera coverage --bbox "-3.0,50.0,2.0,53.0" --year 2024

# Step 2: Download in smaller chunks or use selective bands
# geotessera download --bbox "-3.0,50.0,2.0,53.0" --year 2024 --bands "0,10,20,30,40" --output ./southern_england

# Step 3: Create PCA visualization (handles large datasets efficiently)
# geotessera visualize ./southern_england pca_southern_england.tif --n-components 5

# For Python analysis of large regions:
from geotessera import GeoTessera
import numpy as np

gt = GeoTessera()

# Large region (entire southern England)
bbox = (-3.0, 50.0, 2.0, 53.0)
year = 2024

def process_large_region_efficiently(bbox, year, analysis_func):
    """Process a large region without loading all tiles into memory."""

    # Step 1: Get list of available tiles (metadata only, no data loaded)
    tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=year)
    total_tiles = len(tiles_to_fetch)

    print(f"Processing {total_tiles} tiles...")
    print("Consider using CLI: geotessera download + geotessera visualize for large regions")

    # Step 2: Fetch tiles as generator (one at a time, memory efficient)
    tiles = gt.fetch_embeddings(tiles_to_fetch)

    results = []
    for i, (year, tile_lon, tile_lat, embedding, crs, transform) in enumerate(tiles):
        # Process one tile at a time
        result = analysis_func(embedding, tile_lat, tile_lon)
        results.append(result)

        # Progress indicator
        if (i + 1) % 10 == 0:
            print(f"Processed {i + 1}/{total_tiles} tiles")

        # Free memory
        del embedding

    return results

def vegetation_analysis(embedding, lat, lon):
    """Example analysis function for vegetation detection."""
    # Hypothetical vegetation channels (example)
    veg_channels = [20, 25, 30, 35, 40]

    # Compute vegetation index
    veg_data = embedding[:, :, veg_channels]
    veg_index = np.mean(veg_data, axis=2)

    return {
        'lat': lat,
        'lon': lon,
        'mean_vegetation': float(np.mean(veg_index)),
        'max_vegetation': float(np.max(veg_index)),
        'vegetation_pixels': int(np.sum(veg_index > 0.5))
    }

# Run the analysis
results = process_large_region_efficiently(bbox, year, vegetation_analysis)

# Save results
with open('vegetation_analysis.json', 'w') as f:
    json.dump(results, f, indent=2)

Batch Export for Multiple Regions

Export multiple regions efficiently using CLI commands:

# Use CLI for batch processing
# Create a shell script for batch downloads:

#!/bin/bash

# Check coverage for all regions first (with boundary visualization)
echo "Checking coverage for all regions..."
geotessera coverage --country "United Kingdom" --year 2024 --output uk_coverage.png  # Shows full UK boundaries
geotessera coverage --bbox "-0.3,51.3,0.2,51.7" --year 2024 --output london_coverage.png
geotessera coverage --bbox "-0.2,52.0,0.3,52.3" --year 2024 --output cambridge_coverage.png
geotessera coverage --bbox "-1.4,51.6,-1.1,51.9" --year 2024 --output oxford_coverage.png

# Download regions
echo "Downloading regions..."
geotessera download --bbox "-0.3,51.3,0.2,51.7" --year 2024 --bands "10,20,30,40,50" --output ./batch_exports/london
geotessera download --bbox "-0.2,52.0,0.3,52.3" --year 2024 --output ./batch_exports/cambridge
geotessera download --bbox "-1.4,51.6,-1.1,51.9" --year 2024 --bands "0,1,2" --output ./batch_exports/oxford

# Create PCA visualizations
echo "Creating PCA visualizations..."
geotessera visualize ./batch_exports/london pca_london.tif
geotessera visualize ./batch_exports/cambridge pca_cambridge.tif
geotessera visualize ./batch_exports/oxford pca_oxford.tif

# Create web viewers
echo "Creating web viewers..."
geotessera webmap pca_london.tif --output london_web/ --serve &
geotessera webmap pca_cambridge.tif --output cambridge_web/
geotessera webmap pca_oxford.tif --output oxford_web/

# For Python-based batch processing:
def batch_export_regions(regions_config, base_output_dir):
    """Export multiple regions as GeoTIFF files."""
    import os
    from pathlib import Path

    gt = GeoTessera()

    for region_name, config in regions_config.items():
        print(f"Processing region: {region_name}")
        print(f"Recommend using CLI: geotessera download --bbox '{','.join(map(str, config['bbox']))}' --year {config['year']} --output ./batch_exports/{region_name}")

        output_dir = Path(base_output_dir) / region_name
        output_dir.mkdir(parents=True, exist_ok=True)

        try:
            tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=config['bbox'], year=config['year'])
            files = gt.export_embedding_geotiffs(
                tiles_to_fetch,
                output_dir=str(output_dir),
                bands=config.get('bands', None),
                compress="lzw"
            )

            print(f"  Exported {len(files)} files to {output_dir}")
            print(f"  Next step: geotessera visualize {output_dir} pca_{region_name}.tif")

            # Create metadata file
            metadata = {
                'region': region_name,
                'bbox': config['bbox'],
                'year': config['year'],
                'files': files,
                'band_count': len(config.get('bands', list(range(128)))),
                'next_steps': [
                    f"geotessera visualize {output_dir} pca_{region_name}.tif",
                    f"geotessera webmap pca_{region_name}.tif --serve"
                ]
            }

            with open(output_dir / 'metadata.json', 'w') as f:
                json.dump(metadata, f, indent=2)

        except Exception as e:
            print(f"  Error processing {region_name}: {e}")

# Define regions to process
regions = {
    'london': {
        'bbox': (-0.3, 51.3, 0.2, 51.7),
        'year': 2024,
        'bands': [10, 20, 30, 40, 50]  # Subset of bands
    },
    'cambridge': {
        'bbox': (-0.2, 52.0, 0.3, 52.3),
        'year': 2024,
        'bands': None  # All bands
    },
    'oxford': {
        'bbox': (-1.4, 51.6, -1.1, 51.9),
        'year': 2024,
        'bands': [0, 1, 2]  # RGB only
    }
}

# Run batch export
batch_export_regions(regions, "./batch_exports")

Multi-Year Comparison

Compare embeddings across different years:

def compare_years(lat, lon, years):
    """Compare a single location across multiple years."""
    gt = GeoTessera()

    yearly_data = {}
    for year in years:
        try:
            embedding, crs, transform = gt.fetch_embedding(lon=lon, lat=lat, year=year)

            # Compute summary statistics
            yearly_data[year] = {
                'mean_per_channel': np.mean(embedding, axis=(0, 1)).tolist(),
                'std_per_channel': np.std(embedding, axis=(0, 1)).tolist(),
                'overall_mean': float(np.mean(embedding)),
                'overall_std': float(np.std(embedding))
            }

            print(f"Year {year}: mean={yearly_data[year]['overall_mean']:.3f}")

        except Exception as e:
            print(f"Year {year}: Data not available ({e})")
            yearly_data[year] = None

    return yearly_data

# Compare Cambridge across years
cambridge_comparison = compare_years(
    lat=52.05, lon=0.15,
    years=[2020, 2021, 2022, 2023, 2024]
)

# Save comparison
with open('cambridge_temporal_comparison.json', 'w') as f:
    json.dump(cambridge_comparison, f, indent=2)

# Plot temporal trends
valid_years = [year for year, data in cambridge_comparison.items() if data is not None]
overall_means = [cambridge_comparison[year]['overall_mean'] for year in valid_years]

plt.figure(figsize=(10, 6))
plt.plot(valid_years, overall_means, 'bo-', linewidth=2, markersize=8)
plt.xlabel('Year')
plt.ylabel('Mean Embedding Value')
plt.title('Temporal Trend - Cambridge (52.05°N, 0.15°E)')
plt.grid(True, alpha=0.3)
plt.savefig('cambridge_temporal_trend.png', dpi=150, bbox_inches='tight')
plt.show()

Tutorial 5: Coverage Analysis with Boundary Visualization

Understanding data coverage with precise geographic boundaries.

Visualizing Country Coverage

The coverage command shows precise country boundaries when using --country:

# Countries with simple boundaries
geotessera coverage --country "Germany" --year 2024
geotessera coverage --country "France" --year 2024

# Countries with complex coastlines and islands
geotessera coverage --country "Greece" --year 2024     # Shows all Greek islands
geotessera coverage --country "United Kingdom" --year 2024  # Shows England, Scotland, Wales, N. Ireland
geotessera coverage --country "Indonesia" --year 2024  # Shows thousands of islands

# Using country codes
geotessera coverage --country "UK" --year 2024
geotessera coverage --country "US" --year 2024

Comparing Boundary vs Bounding Box

# Bounding box approach (rectangular, may include unwanted areas)
geotessera coverage --bbox "20,35,30,42" --output greece_bbox.png

# Precise boundary approach (follows actual country borders)
geotessera coverage --country "Greece" --output greece_precise.png

The country approach shows only tiles that actually intersect with Greek territory, excluding tiles over water or neighboring countries that fall within the bounding box.

Tutorial 6: Custom Analysis Workflows

Advanced analysis techniques and custom workflows.

Principal Component Analysis

Reduce dimensionality of the 128-channel embeddings:

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np

def perform_pca_analysis(embeddings_list, n_components=10):
    """Perform PCA on a collection of embedding tiles."""

    # Reshape all embeddings to 2D (pixels x channels)
    all_pixels = []
    tile_info = []

    for year, tile_lon, tile_lat, embedding, crs, transform in embeddings_list:
        # Reshape from (H, W, 128) to (H*W, 128)
        pixels = embedding.reshape(-1, embedding.shape[-1])
        all_pixels.append(pixels)

        # Track which pixels belong to which tile
        n_pixels = pixels.shape[0]
        tile_info.extend([(tile_lat, tile_lon)] * n_pixels)

    # Combine all pixels
    X = np.vstack(all_pixels)
    print(f"Total pixels for PCA: {X.shape[0]:,}")
    print(f"Feature dimensions: {X.shape[1]}")

    # Standardize features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    # Perform PCA
    pca = PCA(n_components=n_components)
    X_pca = pca.fit_transform(X_scaled)

    # Print explained variance
    print(f"\nExplained variance by component:")
    for i, var in enumerate(pca.explained_variance_ratio_):
        print(f"  PC{i+1}: {var:.3f} ({var*100:.1f}%)")
    print(f"Total explained variance: {pca.explained_variance_ratio_.sum():.3f}")

    return X_pca, pca, scaler, tile_info

# Example usage
gt = GeoTessera()
bbox = (-0.1, 51.9, 0.1, 52.1)  # Small region around Cambridge
tiles_to_fetch = gt.registry.load_blocks_for_region(bounds=bbox, year=2024)
tiles = list(gt.fetch_embeddings(tiles_to_fetch))

X_pca, pca, scaler, tile_info = perform_pca_analysis(tiles, n_components=5)

# Visualize first two principal components
plt.figure(figsize=(12, 5))

plt.subplot(1, 2, 1)
plt.scatter(X_pca[:, 0], X_pca[:, 1], alpha=0.5, s=1)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.title('PCA: First Two Components')

plt.subplot(1, 2, 2)
plt.plot(range(1, len(pca.explained_variance_ratio_) + 1),
         pca.explained_variance_ratio_, 'bo-')
plt.xlabel('Principal Component')
plt.ylabel('Explained Variance Ratio')
plt.title('PCA Explained Variance')
plt.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('pca_analysis.png', dpi=150, bbox_inches='tight')
plt.show()

If you would like to now try more advanced classification, go to the Tessera interactive notebook for a Jupyter-based label classifier application.

For cloud-native zarr access to the TESSERA store, see GeoTesseraZarr.