geomaster

# GeoMaster

GeoMaster is a comprehensive geospatial science skill covering the full spectrum of geographic information systems, remote sensing, spatial analysis, and machine learning for Earth observation. This skill provides expert knowledge across 70+ topics with 500+ code examples in 7 programming languages.

## Installation

### Core Python Geospatial Stack

```bash
# Install via conda (recommended for geospatial dependencies)
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas

# Or via uv
uv pip install geopandas rasterio fiona shapely pyproj
```

### Remote Sensing & Image Processing

```bash
# Core remote sensing libraries
uv pip install rsgislib torchgeo eo-learn

# For Google Earth Engine
uv pip install earthengine-api

# For SNAP integration
# Download from: https://step.esa.int/main/download/
```

### GIS Software Integration

```bash
# QGIS Python bindings (usually installed with QGIS)
# ArcPy requires ArcGIS Pro installation

# GRASS GIS
conda install -c conda-forge grassgrass

# SAGA GIS
conda install -c conda-forge saga-gis
```

### Machine Learning for Geospatial

```bash
# Deep learning for remote sensing
uv pip install torch-geometric tensorflow-caney

# Spatial machine learning
uv pip install libpysal esda mgwr
uv pip install scikit-learn xgboost lightgbm
```

### Point Cloud & 3D

```bash
# LiDAR processing
uv pip install laspy pylas

# Point cloud manipulation
uv pip install open3d pdal

# Photogrammetry
uv pip install opendm
```

### Network & Routing

```bash
# Street network analysis
uv pip install osmnx networkx

# Routing engines
uv pip install osrm pyrouting
```

### Visualization

```bash
# Static mapping
uv pip install cartopy contextily mapclassify

# Interactive web maps
uv pip install folium ipyleaflet keplergl

# 3D visualization
uv pip install pydeck pythreejs
```

### Big Data & Cloud

```bash
# Distributed geospatial processing
uv pip install dask-geopandas

# Xarray for multidimensional arrays
uv pip install xarray rioxarray

# Planetary Computer
uv pip install pystac-client planetary-computer
```

### Database Support

```bash
# PostGIS
conda install -c conda-forge postgis

# SpatiaLite
conda install -c conda-forge spatialite

# GeoAlchemy2 for SQLAlchemy
uv pip install geoalchemy2
```

### Additional Programming Languages

```bash
# R geospatial packages
# install.packages(c("sf", "terra", "raster", "terra", "stars"))

# Julia geospatial packages
# import Pkg; Pkg.add(["ArchGDAL", "GeoInterface", "GeoStats.jl"])

# JavaScript (Node.js)
# npm install @turf/turf terraformer-arcgis-parser

# Java
# Maven: org.geotools:gt-main
```

## Quick Start

### Reading Satellite Imagery and Calculating NDVI

```python
import rasterio
import numpy as np

# Open Sentinel-2 imagery
with rasterio.open('sentinel2.tif') as src:
    # Read red (B04) and NIR (B08) bands
    red = src.read(4)
    nir = src.read(8)

    # Calculate NDVI
    ndvi = (nir.astype(float) - red.astype(float)) / (nir + red)
    ndvi = np.nan_to_num(ndvi, nan=0)

    # Save result
    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)

print(f"NDVI range: {ndvi.min():.3f} to {ndvi.max():.3f}")
```

### Spatial Analysis with GeoPandas

```python
import geopandas as gpd

# Load spatial data
zones = gpd.read_file('zones.geojson')
points = gpd.read_file('points.geojson')

# Ensure same CRS
if zones.crs != points.crs:
    points = points.to_crs(zones.crs)

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

# Calculate statistics per zone
stats = joined.groupby('zone_id').agg({
    'value': ['count', 'mean', 'std', 'min', 'max']
}).round(2)

print(stats)
```

### Google Earth Engine Time Series

```python
import ee
import pandas as pd

# Initialize Earth Engine
ee.Initialize(project='your-project-id')

# Define region of interest
roi = ee.Geometry.Point([-122.4, 37.7]).buffer(10000)

# Get Sentinel-2 collection
s2 = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
      .filterBounds(roi)
      .filterDate('2020-01-01', '2023-12-31')
      .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)))

# Add NDVI band
def add_ndvi(image):
    ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
    return image.addBands(ndvi)

s2_ndvi = s2.map(add_ndvi)

# Extract time series
def extract_series(image):
    stats = image.reduceRegion(
        reducer=ee.Reducer.mean(),
        geometry=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'])
print(df.head())
```

## Core Concepts

### Coordinate Reference Systems (CRS)

Understanding CRS is fundamental to geospatial work:

- **Geographic CRS**: EPSG:4326 (WGS 84) - uses lat/lon degrees
- **Projected CRS**: EPSG:3857 (Web Mercator) - uses meters
- **UTM Zones**: EPSG:326xx (North), EPSG:327xx (South) - minimizes distortion

See [coordinate-systems.md](references/coordinate-systems.md) for comprehensive CRS reference.

### Vector vs Raster Data

**Vector Data**: Points, lines, polygons with discrete boundaries
- Shapefiles, GeoJSON, GeoPackage, PostGIS
- Best for: administrative boundaries, roads, infrastructure

**Raster Data**: Grid of cells with continuous values
- GeoTIFF, NetCDF, HDF5, COG
- Best for: satellite imagery, elevation, climate data

### Spatial Data Types

| Type | Examples | Libraries |
|------|----------|-----------|
| Vector | Shapefiles, GeoJSON, GeoPackage | GeoPandas, Fiona, GDAL |
| Raster | GeoTIFF, NetCDF, IMG | Rasterio, GDAL, Xarray |
| Point Cloud | LAZ, LAS, PCD | Laspy, PDAL, Open3D |
| Topology | TopoJSON, TopoArchive | TopoJSON, Network