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RamanEbrahimi
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geomaster

Automate geospatial science tasks across remote sensing, GIS, and spatial analysis.

3|Updated Apr 17, 2026
Search Tags:#machine learning#geospatial#GIS#spatial analysis#remote sensing
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill geomaster-ramanebrahimi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geomaster
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/geomaster
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill geomaster-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill unit solves the complex problems in geospatial science and remote sensing by providing a comprehensive suite of tools, resources, and code examples across various programming languages.

Core Features & Use Cases

  • Comprehensive Coverage: Covers over 70 topics in geospatial science including remote sensing, GIS, spatial analysis, machine learning, and big data.
  • Multilingual Code Examples: Provides practical code examples in Python, R, Julia, JavaScript, C++, Java, Go, and Rust.
  • Use Case: Utilize the Skill unit to perform advanced geospatial computations, such as terrain analysis, machine learning models for remote sensing, and 3D GIS operations.

Quick Start

To install GeoMaster, run:

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

Frequently Asked Questions about geomaster

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

FAQPage Schema
How do I perform spatial analysis and remote sensing tasks across different programming languages?▼

Geospatial analysis and remote sensing workflows can be executed using over 500 examples across Python, R, Julia, JavaScript, C++, Java, Go, and Rust, covering terrain analysis, GIS operations, and machine learning models.

Can I use geopandas and rasterio for machine learning on geospatial big data?▼

Yes, geospatial big data workflows integrate geopandas and rasterio with scikit-learn, xgboost, and torch-geometric to build machine learning models for remote sensing and spatial analysis.

Does this geospatial workflow support 3D GIS operations and point cloud processing?▼

3D GIS operations and point cloud processing are supported through libraries like open3d, pdal, and laspy, enabling advanced terrain analysis and spatial computations.

What is the best way to get started with remote sensing and spatial analysis computations?▼

Getting started involves installing foundational geospatial libraries like gdal, rasterio, fiona, shapely, pyproj, and geopandas via conda to enable spatial analysis and remote sensing computations.

How does Earth Engine API integration work for environmental science data processing?▼

Earth Engine API integration facilitates environmental science data processing by combining earthengine-api and planetary-computer with xarray and dask-geopandas for scalable geospatial big data workflows.

Are there limitations when using PostGIS for spatial analysis versus other geospatial libraries?▼

PostGIS handles spatial database operations, while libraries like shapely and networkx provide specialized spatial analysis and network modeling, offering over 70 topics for diverse geotechnology applications.

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