Caelus provides satellite‑based monitoring that predicts subsidence, landslides, sinkholes, and other structural movements years before they cause damage.
Funding
Funding not disclosed
Founders
Product
Problem
Infrastructure owners and insurers often discover subsidence, landslides, sinkholes, and other structural movements only after damage occurs, because traditional monitoring relies on periodic, ground‑based inspections that are costly, time‑consuming, and limited in coverage.
Solution
Caelus delivers satellite‑based monitoring that continuously tracks ground deformation with 2 mm accuracy using a constellation of more than 30 low‑Earth‑orbit satellites. The platform automatically detects, maps, and alerts users to early signs of subsidence, landslides, sinkholes, and other hazardous movements years before they become critical. By providing a cloud‑hosted data stream tailored to engineering and insurance priorities, Caelus enables proactive risk control, reduces potential damage costs by over 90 %, and cuts inspection time and operational workload. Users can integrate the insights into existing asset‑management workflows, generate risk‑based reports, and prioritize remediation actions without deploying field crews.
Target Audience
Primary customers are infrastructure engineering teams (utilities, transportation, construction) and insurance underwriters or portfolio managers responsible for property and civil‑asset risk assessment.
Features
- Constellation of 30+ satellites delivering global, continuous coverage
- 2 mm ground‑deformation measurement accuracy for high‑resolution monitoring
- Automated detection algorithms that identify subsidence, landslides, sinkholes, and other structural movements
- Real‑time mapping and change‑detection visualizations accessible via a web dashboard
- API and data export tools for seamless integration with engineering and insurance risk‑management systems
- Alerting engine that prioritizes assets based on predicted risk severity
- Reduction of manual inspection effort through AI‑driven anomaly flagging