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GeoMaster Geospatial Science Skill

scientific-skills/geomaster/README.md

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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

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

Remote Sensing

bash
pip install rsgislib torchgeo earthengine-api

Quick Examples

Calculate NDVI from Sentinel-2

python
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

python
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.