Storm Damage Map

Rapid satellite and drone imagery analysis pinpoints hurricane and typhoon damage hotspots, accelerating insurance claims assessment and disaster response planning.

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How does it work?

Manual field assessments after storms are time-consuming and prone to underreporting damages, delaying relief and insurance payouts. Automated geospatial damage mapping leverages high-resolution imagery and AI to deliver fast, accurate loss estimates at scale.

Rapid Damage Detection: AI-driven processing of satellite and drone imagery identifies damaged areas within hours. This acceleration cuts assessment time from days to hours, enabling faster claims initiation.
Accurate Loss Estimates: Machine learning models quantify structural damage and vegetation loss with high precision. This accuracy improves reserve calculations and reduces claim disputes.
Scalable Coverage: Automated workflows analyze thousands of square kilometers without extra manpower. This scalability supports regional to national-scale disaster response planning.
Prioritized Response: Damage severity maps highlight high-impact zones for targeted relief efforts. Emergency teams can allocate resources where they are needed most, improving rescue efficiency.
Cost Efficiency: Automating damage mapping reduces field survey labor and travel expenses. Insurers save on operational costs while maintaining detailed damage records.
Seamless Integration: Results integrate with existing GIS and claims management systems through standard APIs. This ensures smooth data flow and enhances cross-department collaboration.

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 geospatial damage mapping compare to manual surveys?: Unlike manual surveys that take days, geospatial mapping delivers assessments in hours using AI analysis. It also reduces human error and ensures consistent coverage across large areas.
Why is AI-based storm damage mapping popular in insurance?: Insurers adopt it for its speed and accuracy in loss estimation, leading to faster claim resolutions. The technology also supports better risk modeling and reserve planning.
What are the limitations of hurricane damage mapping?: Cloud cover and vegetation canopy can obscure damage detection, requiring image filtering or multispectral data. Very fine structural details may still need ground verification.
What alternatives exist to satellite imagery for damage mapping?: Drone and aerial photography offer higher-resolution data for localized assessments. However, they cover smaller areas and incur higher operational costs compared to satellites.
How often can damage maps be updated after a storm?: Update frequency depends on imagery availability, typically every 1–3 days for commercial satellites and on-demand for drone flights. Automated pipelines process new data immediately upon delivery.
Can this mapping solution integrate with existing GIS platforms?: Yes, it supports standard geospatial formats and APIs for seamless integration with GIS software. This allows departments to overlay damage layers with other spatial data for deeper insights.

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.

Experience

Conversational workflow

Ask questions in plain language and the platform responds with charts, visualizations, and next step suggestions.

Speed

GPU-accelerated cloud

Cloud-native architecture spins up on-demand GPU clusters that scale from a single scene to global archives—no manual ops, no bottlenecks.

Data

Any sensor, any format

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

Visualization

Insight you can see

Real-time 2D / 3D maps and export-ready plots make results clear for engineers, execs, and clients alike.

Boost your productivity. Experience Geospatial AI.

Turn satellite, drone, and sensor data into clear, real-time insights using powerful AI – no complex setup, just answers you can see and act on.

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