What it does. Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task. Part of K-Dense-AI/scientific-agent-skills (AI Scientist skills) (K-Dense-AI/scientific-agent-skills).

Upstream	K-Dense-AI/scientific-agent-skills
Skill file	skills/geomaster/SKILL.md
License	MIT
Author	K-Dense Inc.
Fetched	2026-09-10

Install

• npx skills add K-Dense-AI/scientific-agent-skills --skill geomaster, or copy the skill folder into ~/.claude/skills/geomaster/.
• Raw file: curl -sL https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/geomaster/SKILL.md

SKILL.md (verbatim)

name: geomaster
description: Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
license: MIT License
metadata:
  version: "1.2"
  skill-author: K-Dense Inc.

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

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.

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Other files in this skill

• README.md
• references/advanced-gis.md
• references/big-data.md
• references/code-examples.md
• references/coordinate-systems.md
• references/core-libraries.md
• references/data-sources.md
• references/gis-software.md
• references/industry-applications.md
• references/machine-learning.md
• references/programming-languages.md
• references/remote-sensing.md
• references/scientific-domains.md
• references/specialized-topics.md
• references/troubleshooting.md

README.md (verbatim)

GeoMaster Geospatial Science Skill

Overview

GeoMaster is a comprehensive geospatial science skill covering:

• 70+ sections on geospatial science topics
• 500+ code examples across 7 programming languages
• 300+ geospatial libraries and tools
• Remote sensing, GIS, spatial statistics, ML/AI for Earth observation

Contents

Main Documentation

• SKILL.md - Main skill documentation with installation, quick start, core concepts, common operations, and workflows

Reference Documentation

1. core-libraries.md - GDAL, Rasterio, Fiona, Shapely, PyProj, GeoPandas
2. remote-sensing.md - Satellite missions, optical/SAR/hyperspectral analysis, image processing
3. gis-software.md - QGIS/PyQGIS, ArcGIS/ArcPy, GRASS GIS, SAGA GIS integration
4. scientific-domains.md - Marine, atmospheric, hydrology, agriculture, forestry applications
5. advanced-gis.md - 3D GIS, spatiotemporal analysis, topology, network analysis
6. programming-languages.md - R, Julia, JavaScript, C++, Java, Go geospatial tools
7. machine-learning.md - Deep learning for RS, spatial ML, GNNs, XAI for geospatial
8. big-data.md - Distributed processing, cloud platforms, GPU acceleration
9. industry-applications.md - Urban planning, disaster management, utilities, transportation
10. specialized-topics.md - Geostatistics, optimization, ethics, best practices
11. data-sources.md - Satellite data catalogs, open data repositories, API access
12. code-examples.md - 500+ code examples across 7 programming languages

Key Topics Covered

Remote Sensing

• Sentinel-1/2/3, Landsat, MODIS, Planet, Maxar
• SAR, hyperspectral, LiDAR, thermal imaging
• Spectral indices, classification, change detection

GIS Operations

• Vector data (points, lines, polygons)
• Raster data processing
• Coordinate reference systems
• Spatial analysis and statistics

Machine Learning

• Random Forest, SVM, CNN, U-Net
• Spatial statistics, geostatistics
• Graph neural networks
• Explainable AI

Programming Languages

• Python - GDAL, Rasterio, GeoPandas, TorchGeo, RSGISLib
• R - sf, terra, raster, stars
• Julia - ArchGDAL, GeoStats.jl
• JavaScript - Turf.js, Leaflet
• C++ - GDAL C++ API
• Java - GeoTools
• Go - Simple Features Go

Installation

See SKILL.md for detailed installation instructions.

Core Python Stack

conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas

Remote Sensing

uv pip install rsgislib torchgeo earthengine-api

Quick Examples

Calculate NDVI from Sentinel-2

import rasterio
import numpy as np

with rasterio.open('sentinel2.tif') as src:
    red = src.read(4)
    nir = src.read(8)
    ndvi = (nir - red) / (nir + red + 1e-8)

Spatial Analysis with GeoPandas

import geopandas as gpd

zones = gpd.read_file('zones.geojson')
points = gpd.read_file('points.geojson')
joined = gpd.sjoin(points, zones, predicate='within')

License

MIT License

Author

K-Dense Inc.

Contributing

This skill is part of the K-Dense-AI/scientific-agent-skills repository. For contributions, see the main repository guidelines.

references/advanced-gis.md (verbatim)

Advanced GIS Topics

Advanced spatial analysis techniques: 3D GIS, spatiotemporal analysis, topology, and network analysis.

3D GIS

3D Vector Operations

import geopandas as gpd
from shapely.geometry import Point, LineString, Polygon
import pyproj
import numpy as np

# Create 3D geometries (with Z coordinate)
point_3d = Point(0, 0, 100)  # x, y, elevation
line_3d = LineString([(0, 0, 0), (100, 100, 50)])

# Load 3D data
gdf_3d = gpd.read_file('buildings_3d.geojson')

# Access Z coordinates
gdf_3d['height'] = gdf_3d.geometry.apply(lambda g: g.coords[0][2] if g.has_z else None)

# 3D buffer (cylinder)
def buffer_3d(point, radius, height):
    """Create a 3D cylindrical buffer."""
    base = Point(point.x, point.y).buffer(radius)
    # Extrude to 3D (conceptual)
    return base, point.z, point.z + height

# 3D distance (Euclidean in 3D space)
def distance_3d(point1, point2):
    """Calculate 3D Euclidean distance."""
    dx = point2.x - point1.x
    dy = point2.y - point1.y
    dz = point2.z - point1.z
    return np.sqrt(dx**2 + dy**2 + dz**2)

3D Raster Analysis

import rasterio
import numpy as np

# Voxel-based analysis
def voxel_analysis(dem_path, dsm_path):
    """Analyze volume between DEM and DSM."""
    with rasterio.open(dem_path) as src_dem:
        dem = src_dem.read(1)
        transform = src_dem.transform

    with rasterio.open(dsm_path) as src_dsm:
        dsm = src_dsm.read(1)

    # Height difference
    height = dsm - dem

    # Volume calculation
    pixel_area = transform[0] * transform[4]  # Usually negative
    volume = np.sum(height[height > 0]) * abs(pixel_area)

    # Volume per height class
    height_bins = [0, 5, 10, 20, 50, 100]
    volume_by_class = {}

    for i in range(len(height_bins) - 1):
        mask = (height >= height_bins[i]) & (height < height_bins[i + 1])
        volume_by_class[f'{height_bins[i]}-{height_bins[i+1]}m'] = \
            np.sum(height[mask]) * abs(pixel_area)

    return volume, volume_by_class

Viewshed Analysis

def viewshed(dem, observer_x, observer_y, observer_height=1.7, max_distance=5000):
    """
    Calculate viewshed using line-of-sight algorithm.
    """

    # Convert observer to raster coordinates
    observer_row = int((observer_y - dem_origin_y) / cell_size)
    observer_col = int((observer_x - dem_origin_x) / cell_size)

    rows, cols = dem.shape
    viewshed = np.zeros_like(dem, dtype=bool)

    observer_z = dem[observer_row, observer_col] + observer_height

    # For each direction
    for angle in np.linspace(0, 2*np.pi, 360):
        # Cast ray
        for r in range(1, int(max_distance / cell_size)):
            row = observer_row + int(r * np.sin(angle))
            col = observer_col + int(r * np.cos(angle))

            if row < 0 or row >= rows or col < 0 or col >= cols:
                break

            target_z = dem[row, col]

            # Line-of-sight calculation
            dist = r * cell_size
            line_height = observer_z + (target_z - observer_z) * (dist / max_distance)

            if target_z > line_height:
                viewshed[row, col] = False
            else:
                viewshed[row, col] = True

    return viewshed

Spatiotemporal Analysis

Trajectory Analysis

import movingpandas as mpd
import geopandas as gpd
import pandas as pd

# Create trajectory from point data
gdf = gpd.read_file('gps_points.gpkg')

# Convert to trajectory
traj_collection = mpd.TrajectoryCollection(gdf, 'track_id', t='timestamp')

# Split trajectories (e.g., by time gap)
traj_collection = mpd.SplitByObservationGap(traj_collection, gap=pd.Timedelta('1 hour'))

# Trajectory statistics
for traj in traj_collection:
    print(f"Trajectory {traj.id}:")
    print(f"  Length: {traj.get_length() / 1000:.2f} km")
    print(f"  Duration: {traj.get_duration()}")
    print(f"  Speed: {traj.get_speed() * 3.6:.2f} km/h")

# Stop detection
stops = mpd.stop_detection(
    traj_collection,
    max_diameter=100,  # meters
    min_duration=pd.Timedelta('5 minutes')
)

# Generalization (simplify trajectories)
traj_generalized = mpd.DouglasPeuckerGeneralizer(traj_collection, tolerance=10).generalize()

# Split by stop
traj_moving, stops = mpd.StopSplitter(traj_collection).split()

Space-Time Cube

def create_space_time_cube(gdf, time_column='timestamp', grid_size=100, time_step='1H'):
    """
    Create a 3D space-time cube for hotspot analysis.
    """

    # 1. Spatial binning
    gdf['x_bin'] = (gdf.geometry.x // grid_size).astype(int)
    gdf['y_bin'] = (gdf.geometry.y // grid_size).astype(int)

    # 2. Temporal binning
    gdf['t_bin'] = gdf[time_column].dt.floor(time_step)

    # 3. Create cube (x, y, time)
    cube = gdf.groupby(['x_bin', 'y_bin', 't_bin']).size().unstack(fill_value=0)

    return cube

def emerging_hot_spot_analysis(cube, k=8):
    """
    Emerging Hot Spot Analysis (as implemented in ArcGIS).
    Simplified version using Getis-Ord Gi* statistic.
    """
    from esda.getisord import G_Local

    # Calculate Gi* statistic for each time step
    hotspots = {}
    for timestep in cube.columns:
        data = cube[timestep].values.reshape(-1, 1)
        g_local = G_Local(data, k=k)
        hotspots[timestep] = g_local.p_sim < 0.05  # Significant hotspots

    return hotspots

Topology

Topological Relationships

from shapely.geometry import Point, LineString, Polygon
from shapely.ops import unary_union

# Planar graph
def build_planar_graph(lines_gdf):
    """Build a planar graph from line features."""
    import networkx as nx

    G = nx.Graph()

    # Add nodes at intersections
    for i, line1 in lines_gdf.iterrows():
        for j, line2 in lines_gdf.iterrows():
            if i < j:
                if line1.geometry.intersects(line2.geometry):
                    intersection = line1.geometry.intersection(line2.geometry)
                    G.add_node((intersection.x, intersection.y))

    # Add edges
    for _, line in lines_gdf.iterrows():
        coords = list(line.geometry.coords)
        G.add_edge(coords[0], coords[-1],
                   weight=line.geometry.length,
                   geometry=line.geometry)

    return G

# Topology validation
def validate_topology(gdf):
    """Check for topological errors."""

    errors = []

    # 1. Check for gaps
    if gdf.geom_type.iloc[0] == 'Polygon':
        dissolved = unary_union(gdf.geometry)
        for i, geom in enumerate(gdf.geometry):
            if not geom.touches(dissolved - geom):
                errors.append(f"Gap detected at feature {i}")

    # 2. Check for overlaps
    for i, geom1 in enumerate(gdf.geometry):
        for j, geom2 in enumerate(gdf.geometry):
            if i < j and geom1.overlaps(geom2):
                errors.append(f"Overlap between features {i} and {j}")

    # 3. Check for self-intersections
    for i, geom in enumerate(gdf.geometry):
        if not geom.is_valid:
            errors.append(f"Self-intersection at feature {i}: {geom.is_valid}")

    return errors

Network Analysis

Advanced Routing

import osmnx as ox
import networkx as nx

# Download and prepare network
G = ox.graph_from_place('Portland, Maine, USA', network_type='drive')
G = ox.add_edge_speeds(G)
G = ox.add_edge_travel_times(G)

# Multi-criteria routing
def multi_criteria_routing(G, orig, dest, weights=['length', 'travel_time']):
    """
    Find routes optimizing for multiple criteria.
    """
    # Normalize weights
    for w in weights:
        values = [G.edges[e][w] for e in G.edges]
        min_val, max_val = min(values), max(values)
        for e in G.edges:
            G.edges[e][f'{w}_norm'] = (G.edges[e][w] - min_val) / (max_val - min_val)

    # Combined weight
    for e in G.edges:
        G.edges[e]['combined'] = sum(G.edges[e][f'{w}_norm'] for w in weights) / len(weights)

    # Find path
    route = nx.shortest_path(G, orig, dest, weight='combined')
    return route

# Isochrone (accessibility area)
def isochrone(G, center_node, time_limit=600):
    """
    Calculate accessible area within time limit.
    """
    # Get subgraph of reachable nodes
    subgraph = nx.ego_graph(G, center_node,
                            radius=time_limit,
                            distance='travel_time')

    # Get node geometries
    nodes = ox.graph_to_gdfs(subgraph, edges=False)

    # Create polygon of accessible area
    from shapely.geometry import MultiPoint
    points = MultiPoint(nodes.geometry.tolist())
    isochrone_polygon = points.convex_hull

    return isochrone_polygon, subgraph

# Betweenness centrality (importance of nodes)
def calculate_centrality(G):
    """
    Calculate betweenness centrality for network analysis.
    """
    centrality = nx.betweenness_centrality(G, weight='length')

    # Add to nodes
    for node, value in centrality.items():
        G.nodes[node]['betweenness'] = value

    return centrality

Service Area Analysis

def service_area(G, facilities, max_distance=1000):
    """
    Calculate service areas for facilities.
    """

    service_areas = []

    for facility in facilities:
        # Find nearest node
        node = ox.distance.nearest_nodes(G, facility.x, facility.y)

        # Get nodes within distance
        subgraph = nx.ego_graph(G, node, radius=max_distance, distance='length')

        # Create convex hull
        nodes = ox.graph_to_gdfs(subgraph, edges=False)
        service_area = nodes.geometry.unary_union.convex_hull

        service_areas.append({
            'facility': facility,
            'area': service_area,
            'nodes_served': len(subgraph.nodes())
        })

    return service_areas

# Location-allocation (facility location)
def location_allocation(demand_points, candidate_sites, n_facilities=5):
    """
    Solve facility location problem (p-median).
    """
    from scipy.spatial.distance import cdist

    # Distance matrix
    coords_demand = [[p.x, p.y] for p in demand_points]
    coords_sites = [[s.x, s.y] for s in candidate_sites]
    distances = cdist(coords_demand, coords_sites)

    # Simple heuristic: K-means clustering
    from sklearn.cluster import KMeans

    kmeans = KMeans(n_clusters=n_facilities, random_state=42)
    labels = kmeans.fit_predict(coords_demand)

    # Find nearest candidate site to each cluster center
    facilities = []
    for i in range(n_facilities):
        cluster_center = kmeans.cluster_centers_[i]
        nearest_site_idx = np.argmin(cdist([cluster_center], coords_sites))
        facilities.append(candidate_sites[nearest_site_idx])

    return facilities

For more advanced examples, see code-examples.md.

references/big-data.md (verbatim)

Big Data and Cloud Computing

Distributed processing, cloud platforms, and GPU acceleration for geospatial data.

Distributed Processing with Dask

Dask-GeoPandas

import dask_geopandas
import geopandas as gpd
import dask.dataframe as dd

# Read large GeoPackage in chunks
dask_gdf = dask_geopandas.read_file('large.gpkg', npartitions=10)

# Perform spatial operations
dask_gdf['area'] = dask_gdf.geometry.area
dask_gdf['buffer'] = dask_gdf.geometry.buffer(1000)

# Compute result
result = dask_gdf.compute()

# Distributed spatial join
dask_points = dask_geopandas.read_file('points.gpkg', npartitions=5)
dask_zones = dask_geopandas.read_file('zones.gpkg', npartitions=3)

joined = dask_points.sjoin(dask_zones, how='inner', predicate='within')
result = joined.compute()

Dask for Raster Processing

import dask.array as da
import rasterio

# Create lazy-loaded raster array
def lazy_raster(path, chunks=(1, 1024, 1024)):
    with rasterio.open(path) as src:
        profile = src.profile
        # Create dask array
        raster = da.from_rasterio(src, chunks=chunks)

    return raster, profile

# Process large raster
raster, profile = lazy_raster('very_large.tif')

# Calculate NDVI (lazy operation)
ndvi = (raster[3] - raster[2]) / (raster[3] + raster[2] + 1e-8)

# Apply function to each chunk
def process_chunk(chunk):
    return (chunk - chunk.min()) / (chunk.max() - chunk.min())

normalized = da.map_blocks(process_chunk, ndvi, dtype=np.float32)

# Compute and save
with rasterio.open('output.tif', 'w', **profile) as dst:
    dst.write(normalized.compute())

Dask Distributed Cluster

from dask.distributed import Client

# Connect to cluster
client = Client('scheduler-address:8786')

# Or create local cluster
from dask.distributed import LocalCluster
cluster = LocalCluster(n_workers=4, threads_per_worker=2, memory_limit='4GB')
client = Client(cluster)

# Use Dask-GeoPandas with cluster
dask_gdf = dask_geopandas.from_geopandas(gdf, npartitions=10)
dask_gdf = dask_gdf.set_index(calculate_spatial_partitions=True)

# Operations are now distributed
result = dask_gdf.buffer(1000).compute()

Cloud Platforms

Google Earth Engine

import ee

# Initialize
ee.Initialize(project='your-project')

# Large-scale composite
def create_annual_composite(year):
    """Create cloud-free annual composite."""

    # Sentinel-2 collection
    s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') \
        .filterBounds(ee.Geometry.Rectangle([-125, 32, -114, 42])) \
        .filterDate(f'{year}-01-01', f'{year}-12-31') \
        .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))

    # Cloud masking
    def mask_s2(image):
        qa = image.select('QA60')
        cloud_bit_mask = 1 << 10
        cirrus_bit_mask = 1 << 11
        mask = qa.bitwiseAnd(cloud_bit_mask).eq(0).And(
               qa.bitwiseAnd(cirrus_bit_mask).eq(0))
        return image.updateMask(mask.Not())

    s2_masked = s2.map(mask_s2)

    # Median composite
    composite = s2_masked.median().clip(roi)

    return composite

# Export to Google Drive
task = ee.batch.Export.image.toDrive(
    image=composite,
    description='CA_composite_2023',
    scale=10,
    region=roi,
    crs='EPSG:32611',
    maxPixels=1e13
)
task.start()

Planetary Computer (Microsoft)

import pystac_client
import planetary_computer
import odc.stac
import xarray as xr

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

# Search NAIP imagery
search = catalog.search(
    collections=["naip"],
    bbox=[-125, 32, -114, 42],
    datetime="2020-01-01/2023-12-31",
)

items = list(search.get_items())

# Load as xarray dataset
data = odc.stac.load(
    items[:100],  # Process in batches
    bands=["image"],
    crs="EPSG:32611",
    resolution=1.0,
    chunkx=1024,
    chunky=1024,
)

# Compute statistics lazily
mean = data.mean().compute()
std = data.std().compute()

# Export to COG
import rioxarray
data.isel(time=0).rio.to_raster('naip_composite.tif', compress='DEFLATE')

Google Cloud Storage

from google.cloud import storage
import rasterio
from rasterio.session import GSSession

# Upload to GCS
client = storage.Client()
bucket = client.bucket('my-bucket')
blob = bucket.blob('geospatial/data.tif')
blob.upload_from_filename('local_data.tif')

# Read directly from GCS
with rasterio.open(
    'gs://my-bucket/geospatial/data.tif',
    session=GSSession()
) as src:
    data = src.read()

# Use with Rioxarray
import rioxarray
da = rioxarray.open_rasterio('gs://my-bucket/geospatial/data.tif')

GPU Acceleration

CuPy for Raster Processing

import cupy as cp
import numpy as np

def gpu_ndvi(nir, red):
    """Calculate NDVI on GPU."""
    # Transfer to GPU
    nir_gpu = cp.asarray(nir)
    red_gpu = cp.asarray(red)

    # Calculate on GPU
    ndvi_gpu = (nir_gpu - red_gpu) / (nir_gpu + red_gpu + 1e-8)

    # Transfer back
    return cp.asnumpy(ndvi_gpu)

# Batch processing
def batch_process_gpu(raster_path):
    with rasterio.open(raster_path) as src:
        data = src.read()  # (bands, height, width)

    data_gpu = cp.asarray(data)

    # Process all bands
    for i in range(data.shape[0]):
        data_gpu[i] = (data_gpu[i] - data_gpu[i].min()) / \
                      (data_gpu[i].max() - data_gpu[i].min())

    return cp.asnumpy(data_gpu)

RAPIDS for Spatial Analysis

import cudf
import cuspatial

# Load data to GPU
gdf_gpu = cuspatial.from_geopandas(gdf)

# Spatial join on GPU
points_gpu = cuspatial.from_geopandas(points_gdf)
polygons_gpu = cuspatial.from_geopandas(polygons_gdf)

joined = cuspatial.join_polygon_points(
    polygons_gpu,
    points_gpu
)

# Convert back
result = joined.to_pandas()

PyTorch for Geospatial Deep Learning

import torch
from torch.utils.data import DataLoader

# Custom dataset
class SatelliteDataset(torch.utils.data.Dataset):
    def __init__(self, image_paths, label_paths):
        self.image_paths = image_paths
        self.label_paths = label_paths

    def __getitem__(self, idx):
        with rasterio.open(self.image_paths[idx]) as src:
            image = src.read().astype(np.float32)

        with rasterio.open(self.label_paths[idx]) as src:
            label = src.read(1).astype(np.int64)

        return torch.from_numpy(image), torch.from_numpy(label)

# DataLoader with GPU prefetching
dataset = SatelliteDataset(images, labels)
loader = DataLoader(
    dataset,
    batch_size=16,
    shuffle=True,
    num_workers=4,
    pin_memory=True,  # Faster transfer to GPU
)

# Training with mixed precision
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

for images, labels in loader:
    images, labels = images.to('cuda'), labels.to('cuda')

    with autocast():
        outputs = model(images)
        loss = criterion(outputs, labels)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

Efficient Data Formats

Cloud-Optimized GeoTIFF (COG)

from rio_cogeo.cogeo import cog_translate

# Convert to COG
cog_translate(
    src_path='input.tif',
    dst_path='output_cog.tif',
    dst_kwds={'compress': 'DEFLATE', 'predictor': 2},
    overview_level=5,
    overview_resampling='average',
    config={'GDAL_TIFF_INTERNAL_MASK': True}
)

# Create overviews for faster access
with rasterio.open('output.tif', 'r+') as src:
    src.build_overviews([2, 4, 8, 16], resampling='average')
    src.update_tags(ns='rio_overview', resampling='average')

Zarr for Multidimensional Arrays

import xarray as xr
import zarr

# Create Zarr store
store = zarr.DirectoryStore('data.zarr')

# Save datacube to Zarr
ds.to_zarr(store, consolidated=True)

# Read efficiently
ds = xr.open_zarr('data.zarr', consolidated=True)

# Extract subset efficiently
subset = ds.sel(time='2023-01', latitude=slice(30, 40))

Parquet for Vector Data

import geopandas as gpd

# Write to Parquet (with spatial index)
gdf.to_parquet('data.parquet', compression='snappy', index=True)

# Read efficiently
gdf = gpd.read_parquet('data.parquet')

# Read subset with filtering
import pyarrow.parquet as pq
table = pq.read_table('data.parquet', filters=[('column', '==', 'value')])

For more big data examples, see code-examples.md.

references/code-examples.md (verbatim)

Code Examples

500+ code examples organized by category and programming language.

Python Examples

Core Operations

# 1. Read GeoJSON
import geopandas as gpd
gdf = gpd.read_file('data.geojson')

# 2. Read Shapefile
gdf = gpd.read_file('data.shp')

# 3. Read GeoPackage
gdf = gpd.read_file('data.gpkg', layer='layer_name')

# 4. Reproject
gdf_utm = gdf.to_crs('EPSG:32633')

# 5. Buffer
gdf['buffer_1km'] = gdf.geometry.buffer(1000)

# 6. Spatial join
joined = gpd.sjoin(points, polygons, how='inner', predicate='within')

# 7. Dissolve
dissolved = gdf.dissolve(by='category')

# 8. Clip
clipped = gpd.clip(gdf, mask)

# 9. Calculate area
gdf['area_km2'] = gdf.geometry.area / 1e6

# 10. Calculate length
gdf['length_km'] = gdf.geometry.length / 1000

Raster Operations

# 11. Read raster
import rasterio
with rasterio.open('raster.tif') as src:
    data = src.read()
    profile = src.profile
    crs = src.crs

# 12. Read single band
with rasterio.open('raster.tif') as src:
    band1 = src.read(1)

# 13. Read with window
with rasterio.open('large.tif') as src:
    window = ((0, 1000), (0, 1000))
    subset = src.read(1, window=window)

# 14. Write raster
with rasterio.open('output.tif', 'w', **profile) as dst:
    dst.write(data)

# 15. Calculate NDVI
red = src.read(4)
nir = src.read(8)
ndvi = (nir - red) / (nir + red + 1e-8)

# 16. Mask raster with polygon
from rasterio.mask import mask
masked, transform = mask(src, [polygon.geometry], crop=True)

# 17. Reproject raster
from rasterio.warp import reproject, calculate_default_transform
dst_transform, dst_width, dst_height = calculate_default_transform(
    src.crs, 'EPSG:32633', src.width, src.height, *src.bounds)

Visualization

# 18. Static plot with GeoPandas
gdf.plot(column='value', cmap='YlOrRd', legend=True, figsize=(12, 8))

# 19. Interactive map with Folium
import folium
m = folium.Map(location=[37.7, -122.4], zoom_start=12)
folium.GeoJson(gdf).add_to(m)

# 20. Choropleth
folium.Choropleth(gdf, data=stats, columns=['id', 'value'],
                  key_on='feature.properties.id').add_to(m)

# 21. Add markers
for _, row in points.iterrows():
    folium.Marker([row.lat, row.lon]).add_to(m)

# 22. Map with Contextily
import contextily as ctx
ax = gdf.plot(alpha=0.5)
ctx.add_basemap(ax, crs=gdf.crs)

# 23. Multi-layer map
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
gdf1.plot(ax=ax, color='blue')
gdf2.plot(ax=ax, color='red')

# 24. 3D plot
import pydeck as pdk
pdk.Deck(layers=[pdk.Layer('ScatterplotLayer', data=df)], map_style='mapbox://styles/mapbox/dark-v9')

# 25. Time series map
import hvplot.geopandas
gdf.hvplot(c='value', geo=True, tiles='OSM', frame_width=600)

R Examples

# 26. Load sf package
library(sf)

# 27. Read shapefile
roads <- st_read("roads.shp")

# 28. Read GeoJSON
zones <- st_read("zones.geojson")

# 29. Check CRS
st_crs(roads)

# 30. Reproject
roads_utm <- st_transform(roads, 32610)

# 31. Buffer
roads_buffer <- st_buffer(roads, dist = 100)

# 32. Spatial join
joined <- st_join(roads, zones, join = st_intersects)

# 33. Calculate area
zones$area <- st_area(zones)

# 34. Dissolve
dissolved <- st_union(zones)

# 35. Plot
plot(zones$geometry)

Julia Examples

# 36. Load ArchGDAL
using ArchGDAL

# 37. Read shapefile
data = ArchGDAL.read("countries.shp") do dataset
    layer = dataset[1]
    features = []
    for feature in layer
        push!(features, ArchGDAL.getgeom(feature))
    end
    features
end

# 38. Create point
using GeoInterface
point = GeoInterface.Point(-122.4, 37.7)

# 39. Buffer
buffered = GeoInterface.buffer(point, 1000)

# 40. Intersection
intersection = GeoInterface.intersection(poly1, poly2)

JavaScript Examples

// 41. Turf.js point
const pt1 = turf.point([-122.4, 37.7]);

// 42. Distance
const distance = turf.distance(pt1, pt2, {units: 'kilometers'});

// 43. Buffer
const buffered = turf.buffer(pt1, 5, {units: 'kilometers'});

// 44. Within
const ptsWithin = turf.pointsWithinPolygon(points, polygon);

// 45. Bounding box
const bbox = turf.bbox(feature);

// 46. Area
const area = turf.area(polygon); // square meters

// 47. Along
const along = turf.along(line, 2, {units: 'kilometers'});

// 48. Nearest point
const nearest = turf.nearestPoint(pt, points);

// 49. Interpolate
const interpolated = turf.interpolate(line, 100);

// 50. Center
const center = turf.center(features);

Domain-Specific Examples

Remote Sensing

# 51. Sentinel-2 NDVI time series
import ee
s2 = ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
def add_ndvi(img):
    return img.addBands(img.normalizedDifference(['B8', 'B4']).rename('NDVI'))
s2_ndvi = s2.map(add_ndvi)

# 52. Landsat collection
landsat = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
landsat = landsat.filter(ee.Filter.lt('CLOUD_COVER', 20))

# 53. Cloud masking
def mask_clouds(image):
    qa = image.select('QA60')
    mask = qa.bitwiseAnd(1 << 10).eq(0)
    return image.updateMask(mask)

# 54. Composite
median = s2.median()

# 55. Export
task = ee.batch.Export.image.toDrive(image, 'description', scale=10)

Machine Learning

# 56. Train Random Forest
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=100, max_depth=20)
rf.fit(X_train, y_train)

# 57. Predict
prediction = rf.predict(X_test)

# 58. Feature importance
importances = pd.DataFrame({'feature': features, 'importance': rf.feature_importances_})

# 59. CNN model
import torch.nn as nn
class CNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(4, 32, 3)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.fc = nn.Linear(64 * 28 * 28, 10)

# 60. Training loop
for epoch in range(epochs):
    outputs = model(images)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

Network Analysis

# 61. OSMnx street network
import osmnx as ox
G = ox.graph_from_place('City', network_type='drive')

# 62. Calculate shortest path
route = ox.shortest_path(G, orig_node, dest_node, weight='length')

# 63. Add edge attributes
G = ox.add_edge_speeds(G)
G = ox.add_edge_travel_times(G)

# 64. Nearest node
node = ox.distance.nearest_nodes(G, X, Y)

# 65. Plot route
ox.plot_graph_route(G, route)

Complete Workflows

Land Cover Classification

# 66. Complete classification workflow
def classify_imagery(image_path, training_gdf, output_path):
    from sklearn.ensemble import RandomForestClassifier
    import rasterio
    from rasterio.features import rasterize

    # Load imagery
    with rasterio.open(image_path) as src:
        image = src.read()
        profile = src.profile

    # Extract training data
    X, y = [], []
    for _, row in training_gdf.iterrows():
        mask = rasterize([(row.geometry, 1)], out_shape=image.shape[1:])
        pixels = image[:, mask > 0].T
        X.extend(pixels)
        y.extend([row['class']] * len(pixels))

    # Train
    rf = RandomForestClassifier(n_estimators=100)
    rf.fit(X, y)

    # Predict
    image_flat = image.reshape(image.shape[0], -1).T
    prediction = rf.predict(image_flat)
    prediction = prediction.reshape(image.shape[1], image.shape[2])

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

Flood Mapping

# 67. Flood inundation from DEM
def map_flood(dem_path, flood_level, output_path):
    import rasterio
    import numpy as np

    with rasterio.open(dem_path) as src:
        dem = src.read(1)
        profile = src.profile

    # Identify flooded cells
    flooded = dem < flood_level

    # Calculate depth
    depth = np.where(flooded, flood_level - dem, 0)

    # Save
    with rasterio.open(output_path, 'w', **profile) as dst:
        dst.write(depth.astype(rasterio.float32), 1)

Terrain Analysis

# 68. Slope and aspect from DEM
def terrain_analysis(dem_path):
    import numpy as np
    from scipy import ndimage

    with rasterio.open(dem_path) as src:
        dem = src.read(1)

    # Calculate gradients
    dy, dx = np.gradient(dem)

    # Slope in degrees
    slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 / np.pi

    # Aspect
    aspect = np.arctan2(-dy, dx) * 180 / np.pi
    aspect = (90 - aspect) % 360

    return slope, aspect

Additional Examples (70-100)

# 69. Point in polygon test
point.within(polygon)

# 70. Nearest neighbor
from sklearn.neighbors import BallTree
tree = BallTree(coords)
distances, indices = tree.query(point)

# 71. Spatial index
from rtree import index
idx = index.Index()
for i, geom in enumerate(geometries):
    idx.insert(i, geom.bounds)

# 72. Clip raster
from rasterio.mask import mask
clipped, transform = mask(src, [polygon], crop=True)

# 73. Merge rasters
from rasterio.merge import merge
merged, transform = merge([src1, src2, src3])

# 74. Reproject image
from rasterio.warp import reproject
reproject(source, destination, src_transform=transform, src_crs=crs)

# 75. Zonal statistics
from rasterstats import zonal_stats
stats = zonal_stats(zones, raster, stats=['mean', 'sum'])

# 76. Extract values at points
from rasterio.sample import sample_gen
values = list(sample_gen(src, [(x, y), (x2, y2)]))

# 77. Resample raster
import rasterio
from rasterio.enums import Resampling
resampled = dst.read(out_shape=(src.height * 2, src.width * 2),
                    resampling=Resampling.bilinear)

# 78. Create regular grid
from shapely.geometry import box
grid = [box(xmin, ymin, xmin+dx, ymin+dy)
        for xmin in np.arange(minx, maxx, dx)
        for ymin in np.arange(miny, maxy, dy)]

# 79. Geocoding with geopy
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="geo_app")
location = geolocator.geocode("Golden Gate Bridge")

# 80. Reverse geocoding
location = geolocator.reverse("37.8, -122.4")

# 81. Calculate bearing
from geopy import distance
bearing = distance.geodesic(point1, point2).initial_bearing

# 82. Great circle distance
from geopy.distance import geodesic
d = geodesic(point1, point2).km

# 83. Create bounding box
from shapely.geometry import box
bbox = box(minx, miny, maxx, maxy)

# 84. Convex hull
hull = points.geometry.unary_union.convex_hull

# 85. Voronoi diagram
from scipy.spatial import Voronoi
vor = Voronoi(coords)

# 86. Kernel density estimation
from scipy.stats import gaussian_kde
kde = gaussian_kde(points)
density = kde(np.mgrid[xmin:xmax:100j, ymin:ymax:100j])

# 87. Hotspot analysis
from esda.getisord import G_Local
g_local = G_Local(values, weights)

# 88. Moran's I
from esda.moran import Moran
moran = Moran(values, weights)

# 89. Geary's C
from esda.geary import Geary
geary = Geary(values, weights)

# 90. Semi-variogram
from skgstat import Variogram
vario = Variogram(coords, values)

# 91. Kriging
from pykrige.ok import OrdinaryKriging
OK = OrdinaryKriging(X, Y, Z, variogram_model='spherical')

# 92. IDW interpolation
from scipy.interpolate import griddata
grid_z = griddata(points, values, (xi, yi), method='linear')

# 93. Natural neighbor interpolation
from scipy.interpolate import NearestNDInterpolator
interp = NearestNDInterpolator(points, values)

# 94. Spline interpolation
from scipy.interpolate import Rbf
rbf = Rbf(x, y, z, function='multiquadric')

# 95. Watershed delineation
from scipy.ndimage import label, watershed
markers = label(local_minima)
labels = watershed(elevation, markers)

# 96. Stream extraction
import richdem as rd
rd.FillDepressions(dem, in_place=True)
flow = rd.FlowAccumulation(dem, method='D8')
streams = flow > 1000

# 97. Hillshade
from scipy import ndimage
hillshade = np.sin(alt) * np.sin(slope) + np.cos(alt) * np.cos(slope) * np.cos(az - aspect)

# 98. Viewshed
def viewshed(dem, observer):
    # Line of sight calculation
    visible = np.ones_like(dem, dtype=bool)
    for angle in np.linspace(0, 2*np.pi, 360):
        # Cast ray and check visibility
        pass
    return visible

# 99. Shaded relief
from matplotlib.colors import LightSource
ls = LightSource(azdeg=315, altdeg=45)
shaded = ls.hillshade(elevation, vert_exaggeration=1)

# 100. Export to web tiles
from mercantile import tiles
from PIL import Image
for tile in tiles(w, s, z):
    # Render tile
    pass

For more examples by language and category, refer to the specific reference documents in this directory.

references/core-libraries.md (verbatim)

Core Geospatial Libraries

This reference covers the fundamental Python libraries for geospatial data processing.

GDAL (Geospatial Data Abstraction Library)

GDAL is the foundation for geospatial I/O in Python.

from osgeo import gdal

# Open a raster file
ds = gdal.Open('raster.tif')
band = ds.GetRasterBand(1)
data = band.ReadAsArray()

# Get geotransform
geotransform = ds.GetGeoTransform()
origin_x = geotransform[0]
pixel_width = geotransform[1]

# Get projection
proj = ds.GetProjection()

Rasterio

Rasterio provides a cleaner interface to GDAL.

import rasterio
import numpy as np

# Basic reading
with rasterio.open('raster.tif') as src:
    data = src.read()           # All bands
    band1 = src.read(1)         # Single band
    profile = src.profile       # Metadata

# Windowed reading (memory efficient)
with rasterio.open('large.tif') as src:
    window = ((0, 100), (0, 100))
    subset = src.read(1, window=window)

# Writing
with rasterio.open('output.tif', 'w',
                   driver='GTiff',
                   height=data.shape[0],
                   width=data.shape[1],
                   count=1,
                   dtype=data.dtype,
                   crs=src.crs,
                   transform=src.transform) as dst:
    dst.write(data, 1)

# Masking
with rasterio.open('raster.tif') as src:
    masked_data, mask = rasterio.mask.mask(src, shapes=[polygon], crop=True)

Fiona

Fiona handles vector data I/O.

import fiona

# Read features
with fiona.open('data.geojson') as src:
    for feature in src:
        geom = feature['geometry']
        props = feature['properties']

# Get schema and CRS
with fiona.open('data.shp') as src:
    schema = src.schema
    crs = src.crs

# Write data
schema = {'geometry': 'Point', 'properties': {'name': 'str'}}
with fiona.open('output.geojson', 'w', driver='GeoJSON',
                schema=schema, crs='EPSG:4326') as dst:
    dst.write({
        'geometry': {'type': 'Point', 'coordinates': [0, 0]},
        'properties': {'name': 'Origin'}
    })

Shapely

Shapely provides geometric operations.

from shapely.geometry import Point, LineString, Polygon
from shapely.ops import unary_union

# Create geometries
point = Point(0, 0)
line = LineString([(0, 0), (1, 1)])
poly = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])

# Geometric operations
buffered = point.buffer(1)              # Buffer
simplified = poly.simplify(0.01)        # Simplify
centroid = poly.centroid                 # Centroid
intersection = poly1.intersection(poly2) # Intersection

# Spatial relationships
point.within(poly)      # True if point inside polygon
poly1.intersects(poly2) # True if geometries intersect
poly1.contains(poly2)   # True if poly2 inside poly1

# Unary union
combined = unary_union([poly1, poly2, poly3])

# Buffer with different joins
buffer_round = point.buffer(1, quad_segs=16)
buffer_mitre = point.buffer(1, mitre_limit=1, join_style=2)

PyProj

PyProj handles coordinate transformations.

from pyproj import Transformer, CRS

# Coordinate transformation
transformer = Transformer.from_crs('EPSG:4326', 'EPSG:32633')
x, y = transformer.transform(lat, lon)
x_inv, y_inv = transformer.transform(x, y, direction='INVERSE')

# Batch transformation
lon_array = [-122.4, -122.3]
lat_array = [37.7, 37.8]
x_array, y_array = transformer.transform(lon_array, lat_array)

# Always z/height if available
transformer_always_z = Transformer.from_crs(
    'EPSG:4326', 'EPSG:32633', always_z=True
)

# Get CRS info
crs = CRS.from_epsg(4326)
print(crs.name)  # WGS 84
print(crs.axis_info)  # Axis info

# Custom transformation
transformer = Transformer.from_pipeline(
    'proj=pipeline step inv proj=utm zone=32 ellps=WGS84 step proj=unitconvert xy_in=rad xy_out=deg'
)

GeoPandas

GeoPandas combines pandas with geospatial capabilities.

import geopandas as gpd

# Reading data
gdf = gpd.read_file('data.geojson')
gdf = gpd.read_file('data.shp', encoding='utf-8')
gdf = gpd.read_postgis('SELECT * FROM data', con=engine)

# Writing data
gdf.to_file('output.geojson', driver='GeoJSON')
gdf.to_file('output.gpkg', layer='data', use_arrow=True)

# CRS operations
gdf.crs  # Get CRS
gdf = gdf.to_crs('EPSG:32633')  # Reproject
gdf = gdf.set_crs('EPSG:4326')  # Set CRS

# Geometric operations
gdf['area'] = gdf.geometry.area
gdf['length'] = gdf.geometry.length
gdf['buffer'] = gdf.geometry.buffer(100)
gdf['centroid'] = gdf.geometry.centroid

# Spatial joins
joined = gpd.sjoin(gdf1, gdf2, how='inner', predicate='intersects')
joined = gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000)

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

# Dissolve
dissolved = gdf.dissolve(by='region', aggfunc='sum')

# Clipping
clipped = gpd.clip(gdf, mask_gdf)

# Spatial indexing (for performance)
idx = gdf.sindex
possible_matches = idx.intersection(polygon.bounds)

Common Workflows

Batch Reprojection

import geopandas as gpd
from pathlib import Path

input_dir = Path('input')
output_dir = Path('output')

for shp in input_dir.glob('*.shp'):
    gdf = gpd.read_file(shp)
    gdf = gdf.to_crs('EPSG:32633')
    gdf.to_file(output_dir / shp.name)

Raster to Vector Conversion

import rasterio.features
import geopandas as gpd
from shapely.geometry import shape

with rasterio.open('raster.tif') as src:
    image = src.read(1)
    results = (
        {'properties': {'value': v}, 'geometry': s}
        for s, v in rasterio.features.shapes(image, transform=src.transform)
    )

geoms = list(results)
gdf = gpd.GeoDataFrame.from_features(geoms, crs=src.crs)

Vector to Raster Conversion

from rasterio.features import rasterize
import geopandas as gpd

gdf = gpd.read_file('polygons.gpkg')
shapes = ((geom, 1) for geom in gdf.geometry)

raster = rasterize(
    shapes,
    out_shape=(height, width),
    transform=transform,
    fill=0,
    dtype=np.uint8
)

Combining Multiple Rasters

import rasterio.merge
import rasterio as rio

files = ['tile1.tif', 'tile2.tif', 'tile3.tif']
datasets = [rio.open(f) for f in files]

merged, transform = rasterio.merge.merge(datasets)

# Save
profile = datasets[0].profile
profile.update(transform=transform, height=merged.shape[1], width=merged.shape[2])

with rio.open('merged.tif', 'w', **profile) as dst:
    dst.write(merged)

For more detailed examples, see code-examples.md.

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