geomaster

Process satellite imagery and geospatial data with Python libraries.

Updated Mar 10, 2026

Search Tags:#python #machine learning #data processing #gis #spatial analysis #remote sensing #earth observation

Ready to run

npx skills add https://github.com/felixboehm/biochem-allergy --skill geomaster-felixboehm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: geomaster
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/geomaster
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill geomaster-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gdal, rasterio, geopandas, shapely, pyproj, osmnx, networkx, scikit-learn, torchgeo, pystac-client, planetary-computer, xarray, dask-geopandas, laspy, pylas, open3d, pdal, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for anyone working with geospatial data, from basic GIS operations to advanced remote sensing and machine learning, eliminating the need to juggle multiple specialized tools and libraries.

Core Features & Use Cases

GIS & Remote Sensing: Process vector/raster data, analyze satellite imagery (Sentinel, Landsat, SAR), perform terrain analysis, and more.
Machine Learning: Apply ML/DL models for image classification, segmentation, and spatial prediction.
Use Case: A researcher needs to classify land cover from Sentinel-2 imagery, calculate NDVI, and analyze the results. This Skill can perform all these steps using Python code examples and integrated libraries.

Quick Start

Use the geomaster skill to calculate NDVI from 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 in Python?▼

You calculate NDVI from Sentinel-2 satellite imagery by running Python code that processes raster bands using rasterio and GDAL to output a normalized vegetation index raster.

What Python libraries do I need for remote sensing and spatial analysis workflows?▼

Remote sensing and spatial analysis workflows require Python libraries like GDAL, rasterio, geopandas, shapely, and pyproj to execute vector and raster data processing operations.

Can I apply machine learning models to classify land cover from raster data?▼

Yes, you can apply machine learning models to classify land cover from raster data by leveraging integrated scikit-learn and torchgeo libraries for image classification and spatial prediction.

Does this geospatial workflow support point cloud data processing with LAZ files?▼

This geospatial workflow supports point cloud data processing with LAZ files by utilizing the laspy and pylas dependencies to parse and manipulate 3D spatial data outputs.

What is the best way to handle large raster datasets for Earth observation without memory errors?▼

To handle large raster datasets for Earth observation without memory errors, use xarray and dask-geopandas to parallelize spatial analysis operations and process raster arrays efficiently.

How do I search and access satellite imagery from Microsoft Planetary Computer?▼

To search and access satellite imagery from Microsoft Planetary Computer, use the pystac-client and planetary-computer Python libraries to query STAC catalogs and retrieve remote sensing data assets.