geomaster
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrai
npx skills add LeonChaoX/qinyan-academic-skills --skill geomaster --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# GeoMaster Comprehensive geospatial science skill covering GIS, remote sensing, spatial analysis, and ML for Earth observation across 70+ topics with 500+ code examples in 8 programming languages. ## Installation ```bash # Core Python stack (conda recommended) conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas # Remote sensing & ML uv pip install rsgislib torchgeo earthengine-api uv pip install scikit-learn xgboost torch-geometric # Network & visualization uv pip install osmnx networkx folium keplergl uv pip install cartopy contextily mapclassify # Big data & cloud uv pip install xarray rioxarray dask-geopandas uv pip install pystac-client planetary-computer # Point clouds uv pip install laspy pylas open3d pdal # Databases conda install -c conda-forge postgis spatialite ``` ## Quick Start ### NDVI from Sentinel-2 ```python import rasterio import numpy as np with rasterio.open('sentinel2.tif') as src: red = src.read(4).astype(float) # B04 nir = src.read(8).astype(float) # B08 ndvi = (nir - red) / (nir + red + 1e-8) ndvi = np.nan_to_num(ndvi, nan=0) profile = src.profile profile.update(count=1, dtype=rasterio.float32) with rasterio.open('ndvi.tif', 'w', **prof
- Installation
- Quick Start
- NDVI from Sentinel-2
- Spatial Analysis with GeoPandas
- Google Earth Engine Time Series
- Core Concepts
- Data Types
- Coordinate Systems
- OGC Standards
- Common Operations
- Spectral Indices
- Vector Operations
- Terrain Analysis
- Network Analysis
Core Python stack (conda recommended) conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas Remote sensing & ML uv pip install rsgislib torchgeo earthengine-api uv pip install scikit-learn xgboost torch-geometric Network & visualization uv pip install osmnx networkx folium keplergl uv pip install cartopy contextily mapclassify Big data & cloud uv pip install xarray rioxarray dask-geopandas
What does the geomaster skill do?
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrai
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill geomaster --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
