Engineering
Systems, architecture, and scalable geospatial processing patterns.
Cloud-Native Geospatial
The engineering principles behind cloud-native geospatial systems: range-read formats, catalogs, where compute runs, layout, system boundaries, and reliability.
COGs
How a Cloud Optimized GeoTIFF's tiling, overviews, and header layout make range reads cheap, and the compression, validation, and lifecycle choices that come with it.
GeoParquet
Why columnar storage changes vector reads, how GeoParquet and Parquet's native geospatial types encode geometry and CRS, and when to choose it over other formats.
Raster Processing Pipelines
The stages, data model, provenance, and operating practices of a raster pipeline that turns raw imagery into reproducible analysis-ready products.
Distributed Raster Processing
How raster workloads split across many workers: units of work, scheduler and storage patterns, bottlenecks, correctness at partition boundaries, and operations.
Geospatial ML Pipelines
The engineering loop of geospatial machine learning: building training data, spatially honest splits, preprocessing, inference at scale, and evaluation that yields defensible accuracy and area estimates.
STAC Overview
Operating a STAC catalog: populating the object model, choosing extensions, indexing, validation, versioning, and where STAC sits in a cloud-native architecture.