Python Remote Sensing & Raster 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 three main sections. Core Raster Fundamentals & STAC Mapping covers the architectural foundations: raster data models, CRS transformations, COG internals and authoring, STAC catalog querying with CQL2, band math, and joining rasters to vector geometries for zonal statistics. Satellite Processing Workflows & Index Pipelines takes you end-to-end — clipping, masking, mosaicking, spectral index derivation, temporal aggregation, and defensible change detection. Cloud Execution & Orchestration then scales those pipelines to production with Dask, Prefect, reproducible container images, and cloud clusters on Coiled and AWS Batch.
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 & drift audits
- Writing and validating COGs
- Zonal statistics & vector–raster joins
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
- Advanced resampling & cross-mission grids
- Temporal aggregation & time series
- Change detection & differencing
Cloud Execution & Orchestration
Take local pipeline code to production — Dask clustering, Prefect orchestration, Coiled and AWS Batch, and cost-aware execution for continental-scale raster workloads.
- Scaling raster processing with Dask
- Orchestrating pipelines with Prefect
- Distributed compute on Coiled & AWS Batch
- Tuning Dask chunk sizes for raster cubes
- Optimizing pipeline cost & performance
- Containerizing geospatial environments
- Benchmarking COG read throughput