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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

  1. Storage and Indexing:
    • Use appropriate pixel types for your data
    • Create spatial indexes on raster columns
    • Consider tiling large rasters
  2. 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
  3. 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