PostGIS Raster
Raster Data Support for PostGIS
The postgis_raster
extension adds support for raster (grid-based) spatial data to PostGIS. It enables you to store, analyze, and process raster data such as satellite imagery, elevation models, and other gridded datasets directly in your PostgreSQL database.
Your Nile database arrives with the postgis_raster
extension and its dependency postgis
already enabled.
Understanding Raster Data
A raster consists of a matrix of cells (pixels) organized into rows and columns where each cell contains a value representing information such as:
- Elevation data (DEM - Digital Elevation Model)
- Satellite imagery
- Temperature maps
- Land use classification
- Any other grid-based spatial data
A raster can store multiple layers of data, each layer is called a band. For example a band can represent elevation data, another band can represent temperature data. In satellite imagery, each band typically represents a different wavelength of light (Red, Green, Blue).
Quick Start
Let’s walk through some common operations with raster data.
Creating a Raster Table
Loading Raster Data
Basic Raster Operations
Query pixel values at a specific point:
Calculate statistics for a raster:
Resample a raster to different resolution:
Raster Analysis
Calculate slope from elevation data:
Generate contour lines from elevation data
Raster Properties
Raster Manipulation
Best Practices
-
Storage and Indexing:
- Use appropriate pixel types for your data
- Create spatial indexes on raster columns
- Consider tiling large rasters
-
Performance:
- Use appropriate chunk sizes for large rasters
- Optimize raster resolution for your use case
- Consider using out-db raster storage for very large datasets
-
Data Quality:
- Validate raster data before loading
- Handle NODATA values appropriately
- Use appropriate resampling methods
Common Use Cases
- Digital Elevation Models (DEM)
- Satellite imagery analysis
- Land use/land cover mapping
- Temperature and climate modeling
- Watershed analysis
- Viewshed analysis
- Terrain analysis
- Environmental monitoring
Limitations
- Large raster datasets can consume significant storage
- Processing time increases with raster size
- Memory usage can be high for large raster operations
Additional Resources
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