Mapping an Entire Regional Energy Grid from Space in 72 Hours
- Feasibility costs saved
- $3.5M+Feasibility costs saved
- Unlisted assets discovered
- 2,500+Unlisted assets discovered
- Inference precision
- 93.8%Inference precision
- Instead of six months
- 72 hrsInstead of six months
The challenge
For major energy sector investors, entering an expansive new regional market means flying blind. Our client wanted to assess the commercial viability of acquiring assets across a multi-state grid territory, but local regulatory data was severely outdated and incomplete — leaving critical capital allocation decisions exposed to enormous informational risk.
To protect their investment they needed to know the exact density of operational energy infrastructure across the target region, and precisely how much power each asset was outputting. A manual audit of that magnitude meant helicopter flyovers, dozens of drone survey crews, and analysts scrubbing footage over a projected six-month timeline.
Waiting six months for a conventional consulting sweep meant losing the acquisition window entirely. The client needed to map and quantify an entire region's energy production infrastructure before a single dollar was committed.
What we built
- 01
Autonomous Grid Mapping Engine
Raw regional satellite feeds are processed through a multi-class computer vision detection pipeline that identifies, classifies and precisely maps the physical boundaries of every active solar field, substation cluster and distribution line segment in the target territory. Each discovered asset is tagged with geospatial coordinates, infrastructure class, estimated footprint and operational status.
- 02
Physics-to-Pixel Capacity Model
Hardware-level dimensional data is extracted directly from space-based imagery — panel array geometry, string configurations and physical tilt angles. Those structural parameters are cross-referenced against localised historical irradiance data and seasonal solar angle models to compute a physics-grounded generation estimate for every node in the mapped region.
- 03
Interactive Market Ingress Dashboard
Detection output and capacity modelling are synthesised into a single filterable intelligence layer. Executive teams get verified generation capacity readings, newly discovered unlisted infrastructure, and localised market saturation — compressing what would have been months of field reporting into a boardroom-ready view.
What came out of it
- A six-month projected audit completed in 72 hours.
- Over $3.5M in conventional feasibility and audit cost avoided.
- More than 2,500 operational assets discovered that did not appear in local regulatory records.
- 93.8% inference precision across the mapped territory.
- The acquisition window was met, with capital allocation backed by ground-truthed data.
Capabilities used
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