Subsidence Alert

Continuous high-resolution elevation monitoring detects road subsidence and potential sinkholes, enabling timely alerts and proactive infrastructure maintenance to prevent failures.

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How does it work?

Unpredicted road subsidence and sinkholes pose serious risks to public safety and infrastructure integrity. AI-driven geospatial analytics identify subtle elevation changes early, enabling preemptive repairs and reducing emergency response costs.

Early Detection: The platform analyzes multi-temporal elevation data to spot millimeter-scale ground deformations. Early warnings help agencies schedule inspections before visible damage appears.
Risk Prioritization: Machine learning models assess subsidence severity and predict sinkhole likelihood across road networks. This prioritization optimizes resource allocation for maintenance crews.
Cost Savings: Preventing major collapses lowers emergency repair expenses and traffic disruption costs. Proactive maintenance can reduce overall infrastructure spending by up to 30%.
Automated Monitoring: Automated ingestion of satellite and aerial LiDAR data ensures continuous surveillance without manual checks. Alerts are generated instantly when abnormal deformation patterns emerge.
Custom Alerts: Users define geofences and thresholds to receive tailored notifications via email or SMS. Customizable alert rules ensure stakeholders stay informed of high-risk zones in real-time.
Seamless Integration: APIs integrate analytics outputs into existing asset management systems and GIS platforms. This creates a unified dashboard for decision-making and reporting.

Frequently asked questions

Have a different question and can’t find the answer you’re looking for? Reach out to our support team by sending us an email and we’ll get back to you as soon as we can.

How does this early warning compare to manual inspections?: It automates data analysis and covers large areas more frequently. Manual inspections are costly and less frequent, often missing subtle pre-failure signs.
Why is this approach popular among municipalities?: It reduces unexpected road closures and safety risks by providing timely alerts. Agencies value its cost-effectiveness and data-driven prioritization.
What are the limitations of satellite-based subsidence monitoring?: Satellites can be affected by atmospheric noise and have limited revisit intervals, reducing temporal resolution. Complementary LiDAR or UAV surveys can improve accuracy in high-risk areas.
Can this system detect subsidence under vegetation or pavement?: Radar and LiDAR penetration capabilities allow monitoring beneath surface cover. Dense canopies may still introduce data gaps, requiring adjusted flight paths or ground-based sensors.
How does the platform compare to alternative geotechnical sensors?: Geospatial analytics cover broader areas without installing physical sensors. However, soil moisture probes and inclinometers provide localized validation where needed.
Are there real-time alternatives to periodic satellite passes?: Yes, UAV flights and ground-based radar offer higher-frequency data collection. These methods require dedicated equipment but can deliver near-real-time surveillance of critical zones.

Geospatial AI Platform

Intelligence

AI & foundation models

Deep-learning and foundation models turn raw imagery into ready-to-use insights, so you ship answers instead of training pipelines.

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Data

Any sensor, any format

Optical, SAR, drone, IoT, vector or raster—ingest, fuse, and analyze without conversion headaches.

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