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

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

# Remote sensing & ML
uv pip install rsgislib 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

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

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

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

Type	Examples	Libraries
Vector	Shapefile, GeoJSON, GeoPackage	GeoPandas, Fiona, GDAL
Raster	GeoTIFF, NetCDF, COG	Rasterio, Xarray, GDAL
Point Cloud	LAS, LAZ	Laspy, PDAL, Open3D

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

# 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

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

# 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

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

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

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

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)

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

# 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

Always check CRS before spatial operations
Use projected CRS for area/distance calculations
Validate geometries: gdf = gdf[gdf.is_valid]
Handle missing data: gdf['geometry'] = gdf['geometry'].fillna(None)
Use efficient formats: GeoPackage > Shapefile, Parquet for large data
Apply cloud masking to optical imagery
Preserve lineage for reproducible research
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.

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