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geemu-skill

Build Google Earth Engine Python workflows with geemap for remote sensing analysis.

57|7|Updated Jun 5, 2026
Search Tags:#remote sensing#google earth engine#geemap#dataset lookup#earth engine python#geospatial workflow
One-click install
npx skills add https://github.com/sadassimov/geemu-skill --skill geemu-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: geemu-skill
Source: https://github.com/sadassimov/geemu-skill/tree/main
Command: npx skills add https://github.com/sadassimov/geemu-skill --skill geemu-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires earthengine-api, geemap, geopandas, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

GEEMu removes the friction from writing Google Earth Engine and geemap workflows by guiding the assistant through environment checks, project setup, study-area design, data selection, and reproducible export planning.

Core Features & Use Cases

  • Environment-first workflow: Verifies Python, Earth Engine credentials, geemap availability, and Cloud Project ID before any online task.
  • Research-grade remote sensing support: Helps design study areas, choose boundaries, reason about scales and data layers, and write reproducible Python code for Earth Engine.
  • Practical task coverage: Supports Sentinel-2 NDVI, wildfire recovery, Landsat water-frequency mapping, tiled exports, local GeoTIFF downloads, dataset recommendation, and random forest regression workflows.
  • Documentation and traceability: Produces run logs and data-layer notes so decisions, assumptions, and transformations are recorded alongside the code.

Quick Start

Use GEEMu to help me check my Earth Engine environment, confirm my Cloud Project ID, and write a reproducible geemap workflow for my study area.

Frequently Asked Questions about geemu-skill

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

FAQPage Schema
How do I set up Google Earth Engine and geemap for reproducible remote sensing workflows?▼

Google Earth Engine setup requires verifying Python environments, Earth Engine credentials, geemap availability, and a Cloud Project ID before executing online remote sensing workflows to ensure reproducible study-area processing.

What's the best way to calculate Sentinel-2 NDVI and export rasters using Earth Engine Python?▼

Earth Engine Python workflows support Sentinel-2 NDVI calculation and raster exports by guiding boundary selection, cloud masking, index calculation, local GeoTIFF downloads, and research-grade raster task design.

Can I use geemap to find and lookup datasets for Landsat water-frequency mapping?▼

Geemap supports dataset lookup and recommendation for Landsat water-frequency mapping, helping reason about data layers, scales, and boundaries for remote sensing analysis within Earth Engine workflows.

Does this geospatial workflow approach handle cloud masking and tiled exports for large study areas?▼

Geospatial workflows handle cloud masking and tiled exports for study areas by managing ROI complexity, export scale, and data semantics to ensure reproducible remote sensing analysis and local GeoTIFF downloads.

Why does my Earth Engine Python workflow fail when processing complex study areas with geemap?▼

Earth Engine Python workflows fail without careful handling of data semantics, export scale, ROI complexity, and proper Cloud Project ID initialization, requiring environment verification before geemap processing.

Do I need a Cloud Project ID to run random forest regression workflows with geemap?▼

A Cloud Project ID is required for Earth Engine initialization to run random forest regression workflows, geemap support, and research-grade remote sensing analysis with reproducible export planning.

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