Satellite-based emissions monitoring for industrial operations
GHGSat operates a satellite and aircraft-based emissions monitoring network, built on a geospatial data stack (QGIS, PostGIS, GeoPandas, dbt, Airflow) that processes remote-sensing data into actionable industrial insights. The engineering-heavy hiring mix (5 of 6 open roles) and project focus on constellation scaling and automated payload health management reveal a company in active infrastructure expansion—moving from proof-of-concept toward operational maturity and cost optimization.
GHGSat delivers satellite and aircraft-based emissions monitoring for industrial operators in energy, oil and gas, and mining sectors. The company traces greenhouse gas emissions directly to their source and converts raw remote-sensing data into decision-support products for environmental compliance and operational efficiency. Headquartered in Montreal and founded in 2011, GHGSat has built a technical infrastructure spanning satellite operations, ground-segment processing, and data products. Current scaling challenges center on constellation operations, cost reduction, and automation—areas reflected in their active project pipeline and technical debt reduction priorities.
GHGSat uses C++, Rust, Python for core systems; PostGIS and PostgreSQL for geospatial data; QGIS/GeoPandas/Rasterio for remote-sensing processing; dbt and Airflow for data pipelines; AWS for cloud infrastructure; and ArcGIS Enterprise for geospatial analytics.
GHGSat is scaling its operational satellite constellation, developing automated monitoring and health management systems, building Linux firmware for satellite and airborne payloads, and integrating with government programs—all aimed at reducing operational costs and expanding constellation capacity profitably.
GHGSAT's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.