title, task, lineage_type, upstream_source, upstream_sha, imported_at, prompt_class, upstream_changes, author, validated
| title | task | lineage_type | upstream_source | upstream_sha | imported_at | prompt_class | upstream_changes | author | validated |
|---|---|---|---|---|---|---|---|---|---|
| GeoMaster Geospatial Science Skill | import | https://github.com/K-Dense-AI/scientific-agent-skills/blob/991bd993/skills/geomaster/README.md | 991bd993 | 2026-08-08 | catalogue | accepted | upstream | false |
GeoMaster Geospatial Science Skill
Overview
GeoMaster is a comprehensive geospatial science skill covering:
- 70+ sections on geospatial science topics
- 500+ code examples across 7 programming languages
- 300+ geospatial libraries and tools
- Remote sensing, GIS, spatial statistics, ML/AI for Earth observation
Contents
Main Documentation
- SKILL.md - Main skill documentation with installation, quick start, core concepts, common operations, and workflows
Reference Documentation
- core-libraries.md - GDAL, Rasterio, Fiona, Shapely, PyProj, GeoPandas
- remote-sensing.md - Satellite missions, optical/SAR/hyperspectral analysis, image processing
- gis-software.md - QGIS/PyQGIS, ArcGIS/ArcPy, GRASS GIS, SAGA GIS integration
- scientific-domains.md - Marine, atmospheric, hydrology, agriculture, forestry applications
- advanced-gis.md - 3D GIS, spatiotemporal analysis, topology, network analysis
- programming-languages.md - R, Julia, JavaScript, C++, Java, Go geospatial tools
- machine-learning.md - Deep learning for RS, spatial ML, GNNs, XAI for geospatial
- big-data.md - Distributed processing, cloud platforms, GPU acceleration
- industry-applications.md - Urban planning, disaster management, utilities, transportation
- specialized-topics.md - Geostatistics, optimization, ethics, best practices
- data-sources.md - Satellite data catalogs, open data repositories, API access
- code-examples.md - 500+ code examples across 7 programming languages
Key Topics Covered
Remote Sensing
- Sentinel-1/2/3, Landsat, MODIS, Planet, Maxar
- SAR, hyperspectral, LiDAR, thermal imaging
- Spectral indices, classification, change detection
GIS Operations
- Vector data (points, lines, polygons)
- Raster data processing
- Coordinate reference systems
- Spatial analysis and statistics
Machine Learning
- Random Forest, SVM, CNN, U-Net
- Spatial statistics, geostatistics
- Graph neural networks
- Explainable AI
Programming Languages
- Python - GDAL, Rasterio, GeoPandas, TorchGeo, RSGISLib
- R - sf, terra, raster, stars
- Julia - ArchGDAL, GeoStats.jl
- JavaScript - Turf.js, Leaflet
- C++ - GDAL C++ API
- Java - GeoTools
- Go - Simple Features Go
Installation
See SKILL.md for detailed installation instructions.
Core Python Stack
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas
Remote Sensing
uv pip install rsgislib torchgeo earthengine-api
Quick Examples
Calculate NDVI from Sentinel-2
import rasterio
import numpy as np
with rasterio.open('sentinel2.tif') as src:
red = src.read(4)
nir = src.read(8)
ndvi = (nir - red) / (nir + red + 1e-8)
Spatial Analysis with GeoPandas
import geopandas as gpd
zones = gpd.read_file('zones.geojson')
points = gpd.read_file('points.geojson')
joined = gpd.sjoin(points, zones, predicate='within')
License
MIT License
Author
K-Dense Inc.
Contributing
This skill is part of the K-Dense-AI/scientific-agent-skills repository. For contributions, see the main repository guidelines.