/geomaster

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, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) 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:
    skill-author: K-Dense Inc.

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

# 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

# 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

# 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

# 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

# LiDAR processing
uv pip install laspy pylas

# Point cloud manipulation
uv pip install open3d pdal

# Photogrammetry
uv pip install opendm

Network & Routing

# Street network analysis
uv pip install osmnx networkx

# Routing engines
uv pip install osrm pyrouting

Visualization

# 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

# 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

# PostGIS
conda install -c conda-forge postgis

# SpatiaLite
conda install -c conda-forge spatialite

# GeoAlchemy2 for SQLAlchemy
uv pip install geoalchemy2

Additional Programming Languages

# 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

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

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

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