Affordable Satellite Detection for Insurance: How AI is Replacing Expensive Aerial Surveys

Why Insurance Companies Are Adopting Satellite Object Detection

The Core Problem — Manual Property Inspection Doesn't Scale

Traditional property inspection workflows rely on dispatching adjusters, ordering drone surveys, or purchasing expensive aerial imagery on a per-scene basis. At scale, these approaches break down. A single CAT event — a hurricane, wildfire, or flood — can generate tens of thousands of claims in 48 hours. Sending an adjuster to every affected address isn't operationally or economically feasible.

Satellite imagery has long been the answer to this problem, but the cost and complexity of enterprise platforms like Maxar and Planet Labs has kept it out of reach for all but the largest carriers. A typical Maxar contract starts at $10,000–$50,000/year before any imagery is purchased. For a 50-person insurtech startup, that's a non-starter.

How Satellite AI Changes the Economics of Underwriting and Claims

Modern AI-powered detection APIs change the math entirely. Instead of purchasing and storing raw satellite imagery, insurers query a detection API with an address and receive structured results — building count, vehicle presence, structural change indicators — in seconds, at a cost measured in cents per query.

The key shift: you no longer pay for imagery, you pay for answers. Kestrel AI charges $99/month for 200 address queries, each returning full object detection results. That's $0.50 per property assessment — compared to $50–$500+ per scene from traditional providers.

Key Insurance Use Cases

• Post-catastrophe damage assessment (CAT events) — Triage thousands of affected addresses by satellite-detected structural change before a single adjuster is dispatched
• Property underwriting validation — Verify declared structures, outbuildings, and assets before binding a new policy
• Fraud detection via asset verification — Cross-reference claimed assets (vehicles, equipment) against satellite-detected inventory
• Roof condition and structure change monitoring — Detect additions, deterioration, or unauthorized structures across a portfolio

What to Look for in an Affordable Satellite Detection Platform

Accuracy — mAP Benchmarks Explained

mAP (mean Average Precision) is the standard benchmark for object detection models. It measures how accurately a model detects and localizes objects across all confidence thresholds. For insurance workflows:

• 85%+ mAP is the practical threshold for automated triage use cases
• 88–92% mAP is production-grade for underwriting support
• Most enterprise providers do not publish mAP figures — Kestrel AI's 88.7% mAP is openly disclosed and reproducible

Detection Speed and API Latency

For real-time underwriting and claims triage, latency matters. Batch processing platforms that return results in minutes or hours can't support synchronous workflows. Kestrel AI's sub-3-second end-to-end latency — including imagery fetch, inference, and response — enables live lookups during policy binding calls.

Pricing Model — Per-Search vs. Per-Seat vs. Custom

Enterprise satellite platforms typically charge per scene (imagery purchase) plus annual licensing fees. This creates unpredictable costs that scale poorly with usage spikes (e.g., after a major storm). Per-search pricing — like Kestrel AI's model — aligns cost directly with value: you pay only when you run a detection.

Object Classes Relevant to Insurance

Not all detection platforms cover the object classes insurers need. Key classes for property and casualty insurance:

• Buildings — primary insured structures, outbuildings, sheds
• Vehicles — personal auto, commercial fleet, heavy equipment
• Aircraft — hangar and airport coverage validation
• Ships — marine coverage, port facility monitoring

Kestrel AI detects all four classes from a single API call.

Ease of Integration — No-Hardware SaaS vs. Full Platform

Platforms that require proprietary satellite constellations (Planet Labs, Maxar) demand significant integration work — imagery ordering, storage, preprocessing pipelines — before any detection can happen. Software-only APIs like Kestrel AI require only an HTTPS request with an address. No imagery pipeline. No storage costs. No geospatial expertise required.

Kestrel AI — Satellite Detection Built for Insurance Budgets

Platform Overview

Kestrel AI is a satellite object detection API built on YOLOv8, trained on the SpaceNet dataset (3,851 overhead images) and optimized for the four object classes most relevant to insurance workflows. It accepts a street address, geocodes it, fetches current Google Maps satellite imagery, runs YOLOv8 inference, and returns structured detection results — all in under 3 seconds.

Pricing Tiers

No annual contracts. No per-seat licensing. No overage charges — accounts are prompted to upgrade when they reach their monthly limit, preventing surprise bills.

Detection Capabilities for Insurance Teams

• Input: street address, lat/lng coordinates, or direct image upload
• Output: bounding boxes, class labels, confidence scores per object
• Response format: JSON — integrates directly into claims management systems
• Imagery: Google Maps satellite (current, global coverage)
• Resolution: ~0.6m/pixel at zoom 18 — sufficient for building and vehicle detection

Competitor Comparison

When to Choose Kestrel AI Over Enterprise Platforms

• You need results in seconds, not hours or days
• Your budget is under $2,000/month for satellite detection
• You don't have a geospatial engineering team to manage imagery pipelines
• You need transparent, predictable pricing — no annual contracts
• You're an insurtech startup or mid-market carrier evaluating satellite AI for the first time

Limitations to Know Before You Buy

• Imagery is sourced from Google Maps — not freshly tasked satellite captures. For post-event imagery of a specific date, enterprise providers have an advantage
• Current model covers 4 object classes — not suitable for highly specialized detection tasks (crop monitoring, custom object types)
• Resolution (~0.6m/pixel) is sufficient for building and vehicle detection but not for fine structural damage assessment

Real-World Insurance Workflows

Frequently Asked Questions