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4787f98d98
- Added SKILL.md with installation, quick start, core concepts, workflows - Added 12 reference documentation files covering 70+ topics - Includes 500+ code examples across 7 programming languages - Covers remote sensing, GIS, ML/AI, 30+ scientific domains - MIT License Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> Co-Authored-By: Dr. Umair Rabbani <umairrs@gmail.com>
106 lines
3.2 KiB
Markdown
106 lines
3.2 KiB
Markdown
# GeoMaster Geospatial Science Skill
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## Overview
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GeoMaster is a comprehensive geospatial science skill covering:
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- **70+ sections** on geospatial science topics
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- **500+ code examples** across 7 programming languages
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- **300+ geospatial libraries** and tools
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- Remote sensing, GIS, spatial statistics, ML/AI for Earth observation
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## Contents
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### Main Documentation
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- **SKILL.md** - Main skill documentation with installation, quick start, core concepts, common operations, and workflows
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### Reference Documentation
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1. **core-libraries.md** - GDAL, Rasterio, Fiona, Shapely, PyProj, GeoPandas
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2. **remote-sensing.md** - Satellite missions, optical/SAR/hyperspectral analysis, image processing
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3. **gis-software.md** - QGIS/PyQGIS, ArcGIS/ArcPy, GRASS GIS, SAGA GIS integration
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4. **scientific-domains.md** - Marine, atmospheric, hydrology, agriculture, forestry applications
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5. **advanced-gis.md** - 3D GIS, spatiotemporal analysis, topology, network analysis
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6. **programming-languages.md** - R, Julia, JavaScript, C++, Java, Go geospatial tools
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7. **machine-learning.md** - Deep learning for RS, spatial ML, GNNs, XAI for geospatial
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8. **big-data.md** - Distributed processing, cloud platforms, GPU acceleration
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9. **industry-applications.md** - Urban planning, disaster management, utilities, transportation
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10. **specialized-topics.md** - Geostatistics, optimization, ethics, best practices
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11. **data-sources.md** - Satellite data catalogs, open data repositories, API access
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12. **code-examples.md** - 500+ code examples across 7 programming languages
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## Key Topics Covered
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### Remote Sensing
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- Sentinel-1/2/3, Landsat, MODIS, Planet, Maxar
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- SAR, hyperspectral, LiDAR, thermal imaging
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- Spectral indices, classification, change detection
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### GIS Operations
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- Vector data (points, lines, polygons)
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- Raster data processing
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- Coordinate reference systems
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- Spatial analysis and statistics
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### Machine Learning
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- Random Forest, SVM, CNN, U-Net
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- Spatial statistics, geostatistics
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- Graph neural networks
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- Explainable AI
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### Programming Languages
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- **Python** - GDAL, Rasterio, GeoPandas, TorchGeo, RSGISLib
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- **R** - sf, terra, raster, stars
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- **Julia** - ArchGDAL, GeoStats.jl
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- **JavaScript** - Turf.js, Leaflet
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- **C++** - GDAL C++ API
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- **Java** - GeoTools
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- **Go** - Simple Features Go
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## Installation
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See [SKILL.md](SKILL.md) for detailed installation instructions.
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### Core Python Stack
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```bash
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conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas
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```
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### Remote Sensing
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```bash
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pip install rsgislib torchgeo earthengine-api
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```
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## Quick Examples
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### Calculate NDVI from Sentinel-2
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```python
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import rasterio
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import numpy as np
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with rasterio.open('sentinel2.tif') as src:
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red = src.read(4)
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nir = src.read(8)
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ndvi = (nir - red) / (nir + red + 1e-8)
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```
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### Spatial Analysis with GeoPandas
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```python
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import geopandas as gpd
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zones = gpd.read_file('zones.geojson')
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points = gpd.read_file('points.geojson')
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joined = gpd.sjoin(points, zones, predicate='within')
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```
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## License
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MIT License
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## Author
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K-Dense Inc.
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## Contributing
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This skill is part of the K-Dense-AI/claude-scientific-skills repository.
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For contributions, see the main repository guidelines.
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