Build end-to-end geospatial analysis pipelines for remote sensing and GIS.
npx skills add https://github.com/shushuzn/Rairos --skill geomaster-shushuzn
Please help me install this Agent Skill. Skill: geomaster Source: https://github.com/shushuzn/Rairos/tree/main/skills/geomaster Command: npx skills add https://github.com/shushuzn/Rairos --skill geomaster-shushuzn
GeoMaster helps you transform raw Earth observation data (satellite imagery, DEMs, vector boundaries, and point clouds) into accurate geospatial analyses and machine-learning-ready datasets instead of doing everything manually or piecemeal.
Use the geomaster skill to compute an NDVI GeoTIFF from a Sentinel-2 raster you provide (for example, by reading your input bands, calculating NDVI, and writing the result to an output file).
High-intent search queries and answers about installing and using this skill.
You can compute NDVI from Sentinel-2 imagery by reading the input raster bands, calculating the spectral index, and writing the result to an output GeoTIFF file using geospatial analysis pipelines.
CRS-safe raster and vector analytics ensure correct coordinate reference system transformations during spatial operations, preventing misaligned geographic data. This process applies OGC-style workflows to maintain spatial accuracy across vector and raster computations.
You prepare satellite imagery for land cover classification by building ML-compatible datasets through geospatial pipelines. This involves transforming Earth observation data into structured formats suitable for training spatial machine learning and deep learning models.
Yes, you can process point clouds and DEMs to calculate terrain metrics. The geospatial analysis pipelines support terrain analysis, spatial statistics, and Earth observation data processing to transform raw elevation data into accurate metrics.
Yes, this geospatial analysis approach supports STAC and COG cloud-native data patterns for data access. It handles OGC-style workflows including WMS, WFS, and WCS to facilitate remote sensing and spatial statistics operations.
Python is the primary language for remote sensing and spatial ML workflows, but multi-language guidance is also supported. Examples span R, Julia, JavaScript, C++, Java, Go, and Rust to accommodate diverse geospatial analysis environments.
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LeonChaoXBuild end-to-end geospatial analysis workflows for remote sensing and GIS operations.
