Python Remote Sensing & Raster Processing Pipelines
Production-grade Python patterns for satellite imagery, rasterio/xarray workflows, Cloud-Optimized GeoTIFFs, STAC querying, and batch processing pipelines.
This site is a technical documentation hub for Python-based Earth observation and geospatial data engineering.
It covers the complete raster processing stack — from low-level pixel I/O with rasterio
to multi-dimensional analysis with xarray, lazy cloud-native reads from COGs, and
end-to-end satellite processing pipelines.
Whether you work with Sentinel-2, Landsat, or custom aerial imagery, the patterns here are designed for production environments — distributed Dask clusters, cloud object storage, and fully reproducible analytical workflows. All code examples use the canonical Python remote sensing stack and are validated for correctness.
The content is organised into two main sections. Core Raster Fundamentals & STAC Mapping covers the architectural foundations: raster data models, CRS transformations, COG internals, STAC catalog querying, and band math operations. Satellite Processing Workflows & Index Pipelines takes you end-to-end — clipping, masking, mosaicking, spectral index derivation, and temporal aggregation.
Explore the Documentation
Core Raster Fundamentals & STAC Mapping
Raster data models, COG internals, CRS transformations, STAC querying, band math, pixel resolution and scaling — the architectural bedrock of every raster pipeline.
- Cloud-Optimized GeoTIFF structure
- CRS transformations with rasterio
- Band math operations with xarray
- STAC catalog queries with pystac-client
- Raster metadata extraction
Satellite Processing Workflows & Index Pipelines
End-to-end satellite processing — clipping, cloud masking, resampling, mosaicking, spectral index calculation, and temporal aggregation at continental scale.
- Automated image clipping & cropping
- Cloud & shadow masking (FMask, s2cloudless)
- Spectral index (NDVI, EVI, NDWI) pipelines
- Seamless mosaicking with feathering
- Temporal aggregation & time series