Build, automate, and validate satellite-driven carbon accounting and MRV systems — from raw imagery to audit-ready emissions inventories.
Modern carbon accounting has moved from spreadsheets into distributed, spatially explicit data pipelines. Spatial Pipeline Engineering is a practitioner-focused resource for engineering those pipelines: deterministic geospatial processing, rigorous uncertainty quantification, and cryptographically verifiable audit trails that meet institutional standards.
Written for ESG engineers, climate data scientists, and Python GIS teams, every guide pairs production-ready code with the compliance context behind it — GHG Protocol scoping, ISO 14064, CSRD, and the registry workflows that turn satellite observations into defensible inventories.
The library is organized into four sections covering the full lifecycle: designing MRV architecture, processing satellite imagery for emissions tracking, modeling and validating carbon stocks, and orchestrating and governing the pipelines that tie them together — schema contracts, workflow orchestration, and registry standards. Pick a section below to dive in.
Four sections, each with focused topics and step-by-step Python implementation guides.
Core design patterns for MRV pipelines: spatial alignment, GHG Protocol scoping, data lineage, and registry integration.
Orchestrating MRV pipelines with Airflow/Prefect/Dagster, the canonical data-schema reference, and registry standards & methodologies.
Browse this sectionCloud masking, temporal aggregation, distributed tile processing, and deforestation alerts from Sentinel & Landsat archives.
Carbon stock baselines, LiDAR/SAR biomass fusion, ground-truth alignment, and uncertainty mapping for validated models.