Common Spatial Data Engineering Tasks

Raster Data Processing Using Python Libraries

In a typical geospatial data engineering workflow, the focus is on acquiring, cleaning, transforming, integrating, storing, and preparing spatial data for analysis or machine learning.

A typical workflow for raster data could include:

1. Data Ingestion: Loading spatial datasets from various sources.

Open spatial data can be downloaded from a variety of sources, including the USGS website, state and local government data portals, and OpenTopography. If you are new to working with spatial data and are unsure where to start, please refer to this tutorial on downloading data from OpenTopography.

How to Download Topographic Data from Open Topography

with rasterio.open("Your_data.tif") as src:
 dem = src.read(1)

Examples: GeoTIFFs, LiDAR data, Shapefiles, GeoJSON, PostGIS databases.

2. Metadata Validation and Quality Checks : Checking the coordinate system, spatial extent, resolution, missing values, data consistency, etc.,

 print(src.crs)
 print(src.bounds)…