Geomaster

GeoMaster

Comprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages.

Installation

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# Core Python stack (conda recommended)
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas

# Remote sensing & ML (rsgislib is conda-forge only, not on PyPI)
conda install -c conda-forge rsgislib
uv pip install torchgeo earthengine-api
uv pip install scikit-learn xgboost torch-geometric

# Network & visualization
uv pip install osmnx networkx folium keplergl
uv pip install cartopy contextily mapclassify

# Big data & cloud
uv pip install xarray rioxarray dask-geopandas
uv pip install pystac-client planetary-computer

# Point clouds
uv pip install laspy pylas open3d pdal

# Databases
conda install -c conda-forge postgis spatialite

Quick Start

NDVI from Sentinel-2

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import rasterio
import numpy as np

with rasterio.open('sentinel2.tif') as src:
    red = src.read(4).astype(float)   # B04
    nir = src.read(8).astype(float)   # B08
    ndvi = (nir - red) / (nir + red + 1e-8)
    ndvi = np.nan_to_num(ndvi, nan=0)

    profile = src.profile
    profile.update(count=1, dtype=rasterio.float32)

    with rasterio.open('ndvi.tif', 'w', **profile) as dst:
        dst.write(ndvi.astype(rasterio.float32), 1)

Spatial Analysis with GeoPandas

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import geopandas as gpd

# Load and ensure same CRS
zones = gpd.read_file('zones.geojson')
points = gpd.read_file('points.geojson')

if zones.crs != points.crs:
    points = points.to_crs(zones.crs)

# Spatial join and statistics
joined = gpd.sjoin(points, zones, how='inner', predicate='within')
stats = joined.groupby('zone_id').agg({
    'value': ['count', 'mean', 'std', 'min', 'max']
}).round(2)

Google Earth Engine Time Series

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import ee
import pandas as pd

ee.Initialize(project='your-project')
roi = ee.Geometry.Point([-122.4, 37.7]).buffer(10000)

s2 = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
      .filterBounds(roi)
      .filterDate('2020-01-01', '2023-12-31')
      .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)))

def add_ndvi(img):
    return img.addBands(img.normalizedDifference(['B8', 'B4']).rename('NDVI'))

s2_ndvi = s2.map(add_ndvi)

def extract_series(image):
    stats = image.reduceRegion(ee.Reducer.mean(), roi.centroid(), scale=10, maxPixels=1e9)
    return ee.Feature(None, {'date': image.date().format('YYYY-MM-dd'), 'ndvi': stats.get('NDVI')})

series = s2_ndvi.map(extract_series).getInfo()
df = pd.DataFrame([f['properties'] for f in series['features']])
df['date'] = pd.to_datetime(df['date'])

Core Concepts

Data Types

Coordinate Systems

• EPSG:4326 (WGS 84) - Geographic, lat/lon, use for storage
• EPSG:3857 (Web Mercator) - Web maps only (don't use for area/distance!)
• EPSG:326xx/327xx (UTM) - Metric calculations, <1% distortion per zone
• Use gdf.estimate_utm_crs() for automatic UTM detection

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# Always check CRS before operations
assert gdf1.crs == gdf2.crs, "CRS mismatch!"

# For area/distance calculations, use projected CRS
gdf_metric = gdf.to_crs(gdf.estimate_utm_crs())
area_sqm = gdf_metric.geometry.area

OGC Standards

• WMS: Web Map Service - raster maps
• WFS: Web Feature Service - vector data
• WCS: Web Coverage Service - raster coverage
• STAC: Spatiotemporal Asset Catalog - modern metadata

Common Operations

Spectral Indices

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def calculate_indices(image_path):
    """NDVI, EVI, SAVI, NDWI from Sentinel-2."""
    with rasterio.open(image_path) as src:
        B02, B03, B04, B08, B11 = [src.read(i).astype(float) for i in [1,2,3,4,5]]

    ndvi = (B08 - B04) / (B08 + B04 + 1e-8)
    evi = 2.5 * (B08 - B04) / (B08 + 6*B04 - 7.5*B02 + 1)
    savi = ((B08 - B04) / (B08 + B04 + 0.5)) * 1.5
    ndwi = (B03 - B08) / (B03 + B08 + 1e-8)

    return {'NDVI': ndvi, 'EVI': evi, 'SAVI': savi, 'NDWI': ndwi}

Vector Operations

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# Buffer (use projected CRS!)
gdf_proj = gdf.to_crs(gdf.estimate_utm_crs())
gdf['buffer_1km'] = gdf_proj.geometry.buffer(1000)

# Spatial relationships
intersects = gdf[gdf.geometry.intersects(other_geometry)]
contains = gdf[gdf.geometry.contains(point_geometry)]

# Geometric operations
gdf['centroid'] = gdf.geometry.centroid
gdf['simplified'] = gdf.geometry.simplify(tolerance=0.001)

# Overlay operations
intersection = gpd.overlay(gdf1, gdf2, how='intersection')
union = gpd.overlay(gdf1, gdf2, how='union')

Terrain Analysis

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def terrain_metrics(dem_path):
    """Calculate slope, aspect, hillshade from DEM."""
    with rasterio.open(dem_path) as src:
        dem = src.read(1)

    dy, dx = np.gradient(dem)
    slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 / np.pi
    aspect = (90 - np.arctan2(-dy, dx) * 180 / np.pi) % 360

    # Hillshade
    az_rad, alt_rad = np.radians(315), np.radians(45)
    hillshade = (np.sin(alt_rad) * np.sin(np.radians(slope)) +
                 np.cos(alt_rad) * np.cos(np.radians(slope)) *
                 np.cos(np.radians(aspect) - az_rad))

    return slope, aspect, hillshade

Network Analysis

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import osmnx as ox
import networkx as nx

# Download and analyze street network
G = ox.graph_from_place('San Francisco, CA', network_type='drive')
G = ox.add_edge_speeds(G).add_edge_travel_times(G)

# Shortest path
orig = ox.distance.nearest_nodes(G, -122.4, 37.7)
dest = ox.distance.nearest_nodes(G, -122.3, 37.8)
route = nx.shortest_path(G, orig, dest, weight='travel_time')

Image Classification

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from sklearn.ensemble import RandomForestClassifier
import rasterio
from rasterio.features import rasterize

def classify_imagery(raster_path, training_gdf, output_path):
    """Train RF and classify imagery."""
    with rasterio.open(raster_path) as src:
        image = src.read()
        profile = src.profile
        transform = src.transform

    # Extract training data
    X_train, y_train = [], []
    for _, row in training_gdf.iterrows():
        mask = rasterize([(row.geometry, 1)],
                        out_shape=(profile['height'], profile['width']),
                        transform=transform, fill=0, dtype=np.uint8)
        pixels = image[:, mask > 0].T
        X_train.extend(pixels)
        y_train.extend([row['class_id']] * len(pixels))

    # Train and predict
    rf = RandomForestClassifier(n_estimators=100, max_depth=20, n_jobs=-1)
    rf.fit(X_train, y_train)

    prediction = rf.predict(image.reshape(image.shape[0], -1).T)
    prediction = prediction.reshape(profile['height'], profile['width'])

    profile.update(dtype=rasterio.uint8, count=1)
    with rasterio.open(output_path, 'w', **profile) as dst:
        dst.write(prediction.astype(rasterio.uint8), 1)

    return rf

Modern Cloud-Native Workflows

STAC + Planetary Computer

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import pystac_client
import planetary_computer
import odc.stac

# Search Sentinel-2 via STAC
catalog = pystac_client.Client.open(
    "https://planetarycomputer.microsoft.com/api/stac/v1",
    modifier=planetary_computer.sign_inplace,
)

search = catalog.search(
    collections=["sentinel-2-l2a"],
    bbox=[-122.5, 37.7, -122.3, 37.9],
    datetime="2023-01-01/2023-12-31",
    query={"eo:cloud_cover": {"lt": 20}},
)

# Load as xarray (cloud-native!)
data = odc.stac.load(
    list(search.get_items())[:5],
    bands=["B02", "B03", "B04", "B08"],
    crs="EPSG:32610",
    resolution=10,
)

# Calculate NDVI on xarray
ndvi = (data.B08 - data.B04) / (data.B08 + data.B04)

Cloud-Optimized GeoTIFF (COG)

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import rasterio
from rasterio.session import AWSSession

# Read COG directly from cloud (partial reads)
session = AWSSession(aws_access_key_id=..., aws_secret_access_key=...)
with rasterio.open('s3://bucket/path.tif', session=session) as src:
    # Read only window of interest
    window = ((1000, 2000), (1000, 2000))
    subset = src.read(1, window=window)

# Write COG
with rasterio.open('output.tif', 'w', **profile,
                   tiled=True, blockxsize=256, blockysize=256,
                   compress='DEFLATE', predictor=2) as dst:
    dst.write(data)

# Validate COG
from rio_cogeo.cogeo import cog_validate
cog_validate('output.tif')

Performance Tips

pythonCopy code
# 1. Spatial indexing (10-100x faster queries)
gdf.sindex  # Auto-created by GeoPandas

# 2. Chunk large rasters
with rasterio.open('large.tif') as src:
    for i, window in src.block_windows(1):
        block = src.read(1, window=window)

# 3. Dask for big data
import dask.array as da
dask_array = da.from_rasterio('large.tif', chunks=(1, 1024, 1024))

# 4. Use Arrow for I/O
gdf.to_file('output.gpkg', use_arrow=True)

# 5. GDAL caching
from osgeo import gdal
gdal.SetCacheMax(2**30)  # 1GB cache

# 6. Parallel processing
rf = RandomForestClassifier(n_jobs=-1)  # All cores

Best Practices

1. Always check CRS before spatial operations
2. Use projected CRS for area/distance calculations
3. Validate geometries: gdf = gdf[gdf.is_valid]
4. Handle missing data: gdf['geometry'] = gdf['geometry'].fillna(None)
5. Use efficient formats: GeoPackage > Shapefile, Parquet for large data
6. Apply cloud masking to optical imagery
7. Preserve lineage for reproducible research
8. Use appropriate resolution for your analysis scale

Detailed Documentation

• Coordinate Systems - CRS fundamentals, UTM, transformations
• Core Libraries - GDAL, Rasterio, GeoPandas, Shapely
• Remote Sensing - Satellite missions, spectral indices, SAR
• Machine Learning - Deep learning, CNNs, GNNs for RS
• GIS Software - QGIS, ArcGIS, GRASS integration
• Scientific Domains - Marine, hydrology, agriculture, forestry
• Advanced GIS - 3D GIS, spatiotemporal, topology
• Big Data - Distributed processing, GPU acceleration
• Industry Applications - Urban planning, disaster management
• Programming Languages - Python, R, Julia, JS, C++, Java, Go, Rust
• Data Sources - Satellite catalogs, APIs
• Troubleshooting - Common issues, debugging, error reference
• Code Examples - 500+ examples

GeoMaster covers everything from basic GIS operations to advanced remote sensing and machine learning.

Agent operating procedure

1. Check the environment. Confirm library versions, units, coordinate systems and data formats (FITS, CIF, HDF5, netCDF).
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run on a small grid, region or subset and check physical plausibility.
4. Execute the full task using the instructions and references above.
5. Validate the result. Check units and dimensional consistency, conservation laws or known limits, and numerical convergence.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Report uncertainties and numerical tolerances with every quantitative result.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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