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ml-geomaster

Process satellite imagery and perform geospatial analysis with machine learning.

Updated Mar 13, 2026
Search Tags:#machine learning#geospatial#gis#spatial analysis#remote sensing#earth observation
One-click install
npx skills add https://github.com/biomaps-infra/blender-opencode --skill ml-geomaster
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-geomaster
Source: https://github.com/biomaps-infra/blender-opencode/tree/main/.opencode/skills/ml-geomaster
Command: npx skills add https://github.com/biomaps-infra/blender-opencode --skill ml-geomaster

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for anyone needing to perform geospatial analysis, from basic GIS operations to advanced machine learning on Earth observation data.

Core Features & Use Cases

  • Geospatial Data Processing: Handle vector and raster data, perform spatial analysis, and visualize results.
  • Remote Sensing Workflows: Process satellite imagery (Sentinel, Landsat, SAR), calculate spectral indices, and perform land cover classification.
  • Spatial Machine Learning: Apply ML models for image classification, object detection, and spatiotemporal analysis.
  • Use Case: A researcher needs to analyze changes in forest cover over the last decade using Sentinel-2 imagery. They can use this Skill to download the data, calculate NDVI for each year, classify forest vs. non-forest areas, and quantify the deforestation rate.

Quick Start

Use the ml-geomaster skill to calculate the NDVI from the file 'sentinel2.tif'.

Frequently Asked Questions about ml-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?▼

To calculate NDVI from Sentinel-2 satellite imagery, you can use spatial analysis tools to process the raster data and apply spectral index formulas directly to the imagery bands.

Can I apply machine learning models for land cover classification on raster data?▼

Yes, you can apply spatial machine learning models to raster data for land cover classification, object detection, and spatiotemporal analysis on Earth observation datasets.

What is the best way to perform spatial statistics and vector operations on geospatial data?▼

The best way to perform spatial statistics and vector operations is using a comprehensive geospatial science toolkit that handles both vector and raster data processing within cloud-native workflows.

Does this geospatial analysis approach support processing SAR data and Landsat imagery?▼

Yes, this geospatial analysis approach supports processing SAR data and Landsat imagery alongside Sentinel satellite data for comprehensive remote sensing workflows and Earth observation analysis.

How do I quantify deforestation rates using satellite imagery and spatial analysis?▼

You quantify deforestation rates by downloading satellite imagery, calculating spectral indices like NDVI for each year, classifying forest versus non-forest areas, and analyzing the spatiotemporal changes.

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