🗺️ Raster Processing

Content Outline

Comprehensive guide to raster data processing in PyMapGIS using xarray and rioxarray:

1. Raster Processing Architecture

• xarray and rioxarray integration strategy
• Dask integration for large datasets
• Memory management and chunking
• Cloud-optimized format support (COG, Zarr)
• Performance optimization techniques

2. Core Raster Operations

• reproject(): Coordinate system transformation
• normalized_difference(): Index calculations (NDVI, NDWI, etc.)
• Resampling and aggregation operations
• Masking and clipping operations
• Mathematical operations and band algebra

3. Accessor Pattern for Rasters

• .pmg accessor for xarray DataArrays
• Method chaining for raster workflows
• Integration with xarray ecosystem
• Performance considerations
• Memory efficiency optimization

4. Data Format Support

• GeoTIFF: Standard raster format handling
• Cloud Optimized GeoTIFF (COG): Optimized cloud access
• NetCDF: Multi-dimensional scientific data
• Zarr: Cloud-native array storage
• HDF5: Hierarchical data format support

5. Coordinate Reference Systems

• CRS handling for raster data
• Reprojection algorithms and optimization
• Pixel alignment and registration
• Accuracy preservation during transformation
• Integration with GDAL and PROJ

6. Large Dataset Processing

• Dask integration for out-of-core processing
• Chunking strategies and optimization
• Parallel processing implementation
• Memory usage monitoring and optimization
• Progress tracking for long operations

7. Cloud-Native Processing

• Cloud Optimized GeoTIFF (COG) optimization
• Zarr array processing
• Remote data access optimization
• Streaming and partial data loading
• Cloud storage integration

8. Raster-Vector Integration

• Raster-vector overlay operations
• Zonal statistics calculation
• Vector-based masking and clipping
• Rasterization of vector data
• Vectorization of raster data

9. Multi-dimensional Data

• Time series raster processing
• Multi-band image processing
• Hyperspectral data handling
• 3D raster data support
• Temporal aggregation and analysis

10. Performance Optimization

• Memory usage profiling and optimization
• I/O optimization strategies
• Parallel processing implementation
• Caching strategies for raster data
• Benchmarking and performance monitoring

11. Quality Assurance

• Data validation and quality checks
• Nodata handling and masking
• Accuracy assessment procedures
• Error detection and reporting
• Metadata validation and preservation

12. Visualization Integration

• Raster visualization pipeline
• Color mapping and styling
• Interactive raster exploration
• Export capabilities
• Integration with mapping libraries

13. Testing and Validation

• Unit tests for raster operations
• Performance regression testing
• Accuracy validation procedures
• Edge case handling
• Integration test scenarios

14. Future Enhancements

• Additional raster operations
• Performance improvement opportunities
• New format support
• Advanced analysis capabilities
• Machine learning integration

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This guide will provide detailed technical information on raster processing implementation, optimization strategies, and best practices for working with raster data in PyMapGIS.