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. U

Guidance and local audit tools for Python workflows that directly use GeoPandas GeoSeries, GeoDataFrame, spatial operations, or vector-data I/O.

Patterns for visualizing data on maps including choropleth maps, heat maps, 3D visualizations, data-driven styling, and animated data. Covers layer types, color scales, and performance optimization.

Common style patterns, layer configurations, and recipes for typical mapping scenarios including restaurant finders, real estate, data visualization, navigation, delivery/logistics, and more. Use when implementing specific map use cases or looking for proven style patterns.