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, poin
Оригинален текст · няма потвърден превод на българскиПърво прочетете инструкцията.
Файлът се зарежда от посочения източник само по ваше действие. Можете да го прочетете тук, да го копирате или да го запазите като .md.
GitHub хранилище- Звезди на репото
- 27 938
- Лиценз
- Не е посочен. Проверете в репозитория.
- Последна проверка на записа
- Няма посочена дата
Добавете в своя инструмент.
Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add K-Dense-AI/claude-scientific-skillsСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
Откъс от SKILL.md
Началото на файла на автора, без превод.
GeoMaster
Geospatial analysis across vector/raster GIS, remote sensing, spatial ML, terrain, networks, and scientific applications. Start with the relevant workflow, inspect input provenance, and load only the reference needed for the task.
Tested scope and installation
The local recipe suite targets GeoPandas 1.2.0, Rasterio 1.5.2, Shapely 2.1.2, PyProj 3.8.0, Rioxarray 0.23.0, Xarray 2026.9.0, OSMnx 2.1.1 and PySTAC Client 0.9.0. See review and source ledger for execution limits. Examples requiring actual input files are templates; authenticated Earth Engine/CDS/commercial services, desktop GIS and GPU training remain illustrative.
# In a dedicated environment; install only the workflow's optional dependencies.
uv venv --python 3.13
uv pip install geopandas==1.2.0 rasterio==1.5.2 shapely==2.1.2 pyproj==3.8.0
uv pip install rioxarray==0.23.0 xarray==2026.9.0 'dask[array]' scikit-learn==1.9.1
uv pip install pystac-client==0.9.0 planetary-computer==1.0.0 odc-stac
# For osgeo/native CLI or PDAL, use a separate conda-forge environment:
# conda create -n geo-native -c conda-forge python=3.13 gdal pdal python-pdal
Install TorchGeo/PyTorch, RSGISLib, Py6S/6S, ArcPy, QGIS or other specialist
runtimes separately when required. A Rasterio wheel includes its own GDAL library;
it does not install osgeo or the GDAL command-line programs.
Workflow
- Record product/collection ID, acquisition time, processing baseline, license, band names, scale/offset, QA meaning, horizontal/vertical CRS and units.
- Verify CRS from authoritative metadata.
set_crslabels coordinates;to_crstransforms them. Never infer an unknown CRS from plausible bounds. - Align extent, affine transform, dimensions, pixel registration and resolution. Reproject categorical masks with nearest-neighbor resampling; choose an appropriate resampler for continuous data. Matching array shape alone does not prove alignment.
- Preserve nodata, clouds, shadows and saturation masks. Convert unsigned integers to floating point before differences; apply the provider's radiometric transform once.
- Run the analysis at a defensible support/resolution. Keep training labels and validation blocks separate. Terrain elevation and horizontal units must agree.
- Export CRS, transform, valid-data mask, units, model settings and provenance. Check numeric expectations on a small known fixture before scaling up.