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geomaster

Process satellite imagery and geospatial data across seven programming languages.

13|3|Updated Jun 10, 2026
Search Tags:#machine learning#data analysis#GIS#spatial analysis#remote sensing#geospatial tools#geospatial science
One-click install
npx skills add https://github.com/tassiovale/claude-code-kit --skill geomaster-tassiovale
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geomaster
Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/geomaster
Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill geomaster-tassiovale

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gdal, rasterio, fiona, shapely, pyproj, geopandas, rsgislib, torchgeo, earthengine-api, scikit-learn, xgboost, torch-geometric, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

GeoMaster addresses the complex challenges of geospatial science by providing a comprehensive suite of tools and examples for remote sensing, GIS, spatial analysis, and machine learning.

Core Features & Use Cases

  • Comprehensive Coverage: 70+ sections on geospatial science topics, 500+ code examples in 7 programming languages, and 300+ geospatial libraries and tools.
  • Remote Sensing: Process satellite imagery (Sentinel, Landsat, MODIS, SAR, hyperspectral), perform spectral analysis, and change detection.
  • GIS Operations: Vector and raster data processing, coordinate reference systems, spatial analysis, and statistics.
  • Machine Learning: Deep learning for remote sensing, spatial ML, GNNs, and XAI for geospatial tasks.
  • Use Case: A geospatial scientist needs to analyze satellite imagery for land cover classification. GeoMaster provides the necessary tools, including libraries and example code, to accomplish this task.

Quick Start

Use the geomaster skill to calculate the NDVI from Sentinel-2 imagery in the file 'sentinel2.tif'.

Frequently Asked Questions about geomaster

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate NDVI from Sentinel-2 satellite imagery?▼

NDVI calculation from Sentinel-2 imagery involves loading raster bands and applying spectral analysis formulas. GeoMaster provides 500+ code examples covering satellite imagery processing in Python, R, Julia, and other languages.

What's the best way to perform land cover classification using machine learning?▼

Land cover classification using machine learning leverages scikit-learn, XGBoost, and torch-geometric for spatial ML and GNNs. GeoMaster provides example code for deep learning applied to remote sensing and geospatial tasks.

Does this support vector and raster data operations with coordinate reference systems?▼

Vector and raster data operations with coordinate reference systems are supported through geopandas, fiona, shapely, rasterio, and pyproj. GeoMaster covers GIS operations, spatial analysis, and spatial statistics comprehensively.

Can I use Google Earth Engine for cloud-native geospatial workflows?▼

Google Earth Engine is supported via the earthengine-api dependency for cloud-native geospatial workflows. GeoMaster provides code examples for processing satellite imagery from Sentinel, Landsat, MODIS, SAR, and hyperspectral sources.

Do I need Python to use these geospatial analysis tools, or are other languages supported?▼

Python is not required as GeoMaster provides 500+ code examples across 7 programming languages including Python, R, Julia, JavaScript, C++, Java, Go, and Rust, covering 300+ geospatial libraries and tools.

What libraries are available for point cloud processing and network analysis?▼

Point cloud processing and network analysis are covered using libraries such as rsgislib and torch-geometric. GeoMaster includes 70+ sections on geospatial science topics with spatial statistics and advanced spatial analysis operations.

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