The investment world lacks a unified standard for measuring Environmental Social and Governance (ESG) performance, and most ratings depend on self-reporting.
Satellite imagery has long been a factor in valuation models, but has relied on highly manual, time-intensive processes to extract information on ground conditions.
The EarthAI platform applies machine learning to remote sensing data to generate an objective assessment in near-real-time, so investors can make better decisions sooner.
Observe locations remotely
Develop automated workflows
Access data via API
Share data across enterprise
Pulse by Astraea
Pulse delivers monitoring solutions that predict commercial outcomes.
By combining our machine learning techniques with mobile data and multiple imagery types, we provide insight into ground conditions more frequently and at a fraction of the cost of legacy methods.
Using our Pulse product, asset managers can understand factory output, theme park attendance, construction progress, retail performance, and various custom solutions.
Make better investment decisions by applying machine learning to remote sensing data.
Investors increasingly require asset managers to report on ESG outcomes; some require performance against ESG goals. Yet few leverage reliable, objective assessments.
Analysts use EarthAI to investigate company-specific and macroeconomic indicators relevant to specific investments.
With remote sensing, ratings agencies can validate self-reported metrics with objective observations of the physical world.
Green Bond Verification
Green bond issuance continues to expand, as investors capitalize on low interest rates to finance projects with a positive sustainable outcome.
Executing these projects and collecting coupon payments often requires verification of remotely observable phenomena.
EarthAI can monitor and evaluate the environmental outcome, without sending auditors out into the field.