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geomaster

Analyze Earth observation data with geospatial libraries and spatial machine learning.

2|Updated Jun 4, 2026
Search Tags:#machine learning#geospatial#gis#spatial analysis#remote sensing#earth observation
One-click install
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill geomaster-lord1egypt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geomaster
Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/geomaster
Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill geomaster-lord1egypt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gdal, rasterio, fiona, shapely, pyproj, geopandas, rsgislib, torchgeo, earthengine-api, scikit-learn, xgboost, torch-geometric, osmnx, networkx, folium, keplergl, cartopy, contextily, mapclassify, xarray, rioxarray, dask-geopandas, pystac-client, planetary-computer, laspy, pylas, open3d, pdal, and includes references (resource) components.

What problem does it solve?

GeoMaster solves the complexity of navigating fragmented geospatial libraries and workflows by providing a unified, expert-level guide for processing satellite imagery, vector data, and spatial machine learning models.

Core Features & Use Cases

  • Multi-Domain Analysis: Perform advanced remote sensing, hydrological modeling, and atmospheric science tasks using a single, cohesive toolkit.
  • Cloud-Native Workflows: Seamlessly integrate with STAC catalogs, COG formats, and cloud platforms like Microsoft Planetary Computer and Google Earth Engine.
  • Cross-Language Support: Access 500+ code examples across 8 programming languages, including Python, R, Julia, and C++, to ensure compatibility with any existing research stack.

Quick Start

Use the geomaster skill to calculate the NDVI index from the provided Sentinel-2 satellite imagery file.

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 using Python?▼

GeoMaster provides comprehensive geospatial analysis capabilities for remote sensing and spatial machine learning, supporting satellite imagery processing, terrain analysis, and cloud-native Earth observation data access.

Can I use Google Earth Engine and Microsoft Planetary Computer for cloud-native geospatial workflows?▼

Yes, GeoMaster supports cloud-native workflows by integrating with STAC catalogs, COG formats, Microsoft Planetary Computer, and Google Earth Engine for seamless satellite imagery processing and spatial analysis.

What's the best way to process LiDAR point cloud data for terrain analysis?▼

The best way is using GeoMaster's unified toolkit, which covers remote sensing, GIS operations, and spatial machine learning for Earth observation data, replacing fragmented geospatial libraries and workflows.

Does this geospatial analysis approach support spatial machine learning with PyTorch?▼

GeoMaster requires standard geospatial libraries like GDAL, Rasterio, GeoPandas, and machine learning frameworks including scikit-learn, xgboost, torch-geometric, and torchgeo for spatial computation.

How do I visualize spatial analysis results with interactive maps?▼

GeoMaster supports cross-language workflows with 500+ code examples across 8 programming languages including Python, R, Julia, and C++, ensuring compatibility with existing research stacks.

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