Satellite Building Detection API: How It Works, Top Providers, and Pricing (2026)

What Is a Satellite Building Detection API?

A satellite building detection API is a software service that accepts satellite or aerial imagery as input and returns the locations, bounding boxes, and classifications of objects detected in that image — buildings, vehicles, aircraft, ships, or other structures — without requiring you to own or operate any satellite hardware.

You send an HTTP request (typically with an image URL, coordinates, or a base64-encoded image), and the API returns a JSON response containing detection results. The entire round-trip takes seconds rather than the hours or days required by traditional satellite tasking workflows.

How Building Detection Differs from General Object Detection

Standard computer vision models like YOLO are trained on ground-level imagery (COCO dataset: dogs, cars, people). Satellite building detection models are specifically trained on overhead imagery at varying altitudes and zoom levels, requiring specialized datasets and augmentation strategies to handle:

• Top-down perspective (no front-facing features)
• Rotation invariance (buildings appear at all angles)
• Scale variance (imagery from 100m vs 1,000m altitude)
• Atmospheric and lighting conditions across geographies
• Small object sizes relative to image resolution

Kestrel AI's model was trained on the SpaceNet dataset — over 3,800 overhead images — specifically optimized for these challenges.

Key Use Cases by Industry

• Property insurance: Post-catastrophe damage triage, roof condition assessment, underwriting validation
• Insurtech: Automated property risk scoring at scale, fraud detection via asset verification
• Construction: Site progress monitoring, equipment tracking, unauthorized structure detection
• Real estate: Portfolio-level property assessment, neighborhood change detection
• Government / defense: GEOINT, base monitoring, SBIR-eligible geospatial intelligence workflows

How Kestrel AI's Satellite Building Detection API Works

YOLOv8 Architecture and Why It Matters

Kestrel AI is built on YOLOv8 (You Only Look Once v8), the current state-of-the-art single-stage object detection architecture from Ultralytics. Unlike two-stage detectors (e.g., Faster R-CNN), YOLOv8 processes the entire image in a single forward pass, making it ideal for real-time API use cases where latency matters.

The architecture delivers an exceptional speed-accuracy tradeoff — 88.7% mAP on our satellite imagery benchmark while maintaining sub-3-second end-to-end latency including image fetching, preprocessing, inference, and response serialization.

88.7% mAP — What This Score Means in Practice

mAP (mean Average Precision) is the standard benchmark for object detection models. It measures the model's precision across all detection confidence thresholds and object classes. An 88.7% mAP means:

• ~89 out of 100 buildings present in an image are correctly detected and localized
• False positive rate is low enough for production underwriting and monitoring workflows
• Performance holds across different geographies and building types in the training set

For comparison, most enterprise satellite intelligence platforms do not publicly disclose mAP benchmarks — making Kestrel AI's transparency a meaningful differentiator for technical buyers.

Sub-3-Second Detection: Speed Benchmarks

End-to-end latency from API request to JSON response is under 3 seconds for standard satellite imagery inputs. This includes:

• Image retrieval from Google Maps Static API
• Preprocessing and normalization
• YOLOv8 inference on CPU (Hugging Face Spaces, cpu-basic)
• Response serialization and return

This makes Kestrel AI suitable for synchronous workflows — a claims adjuster can get detection results while still on the phone with a policyholder.

Supported Object Classes

The current model detects four object classes from a single API endpoint:

• Buildings — residential, commercial, industrial structures
• Vehicles — cars, trucks, heavy equipment
• Aircraft — planes, helicopters visible on tarmac or runways
• Ships — vessels visible in ports, harbors, or open water

Multi-class detection from a single request means insurers can assess both structure and asset inventory from one satellite image query.

Satellite Building Detection API Pricing

Hidden Costs to Watch For With Enterprise Providers

Enterprise satellite intelligence platforms like Maxar, Planet Labs, and EOSDA typically bundle several cost layers that aren't visible in initial quotes:

• Imagery tasking fees: $300–$2,000+ per km² for new satellite captures
• Archive access fees: Charged per scene, often $50–$500/image
• Annual contract minimums: Most require $10,000–$50,000/year commitments
• Professional services: Onboarding, custom model training, integration support
• Per-seat licensing: User-based rather than usage-based pricing

Kestrel AI uses transparent, usage-based pricing with no contracts, no hidden fees, and monthly billing. You pay for searches used, not seats occupied.

Cost Comparison: Kestrel AI vs. Enterprise Platforms

Satellite Building Detection API Comparison

Industry Use Cases With Measurable Outcomes

Property Insurance & Insurtech: Automating Underwriting

Insurance carriers and insurtechs use satellite building detection to automate property validation at underwriting. Instead of dispatching an inspector to every new policy address, an underwriter can query the Kestrel AI API with an address and receive a satellite-derived building count, structure type estimate, and vehicle presence — in under 3 seconds.

Practical applications include:

• Verifying declared structures match satellite-visible buildings before binding
• Post-CAT (catastrophe) damage triage: prioritizing claims by detected structural change
• Ongoing portfolio monitoring for unreported additions or outbuildings

Construction Monitoring: Tracking Site Progress via Satellite

Construction project managers use the API to track progress at remote or multi-site projects without sending personnel. By running weekly satellite queries on the same coordinates and diffing the detection results, teams can monitor:

• Whether new structures have appeared (or been removed)
• Equipment and vehicle presence as a proxy for workforce activity
• Unauthorized construction or encroachments on adjacent parcels

Real Estate Analytics: Portfolio-Scale Property Assessment

Real estate analytics firms use satellite detection APIs to characterize properties at portfolio scale — thousands of addresses processed in hours rather than months. Detection results feed valuation models, risk scores, and market intelligence dashboards.

Government & Defense: SBIR-Eligible Deployment

Kestrel AI's architecture and pricing make it eligible for SBIR (Small Business Innovation Research) program funding. Federal agencies and defense contractors can use SBIR Phase I and II grants to pilot and scale satellite AI capabilities without the multi-year procurement cycles required for prime contractor solutions like Maxar.

How to Integrate the Kestrel AI Building Detection API

API Authentication and Endpoint Structure

Authentication uses a Supabase JWT token obtained via standard email/password or OAuth sign-in. Pass the token as a Bearer header on every request:

POST https://obdsgqjkjjmmtbcfjhnn.supabase.co/functions/v1/detect
Authorization: Bearer <your-jwt-token>
Content-Type: application/json

Input: Address-Based Request

{
  "address": "1600 Amphitheatre Parkway, Mountain View, CA",
  "zoom": 18,
  "source": "google_maps"
}

Sample Response Output

{
  "address": "1600 Amphitheatre Parkway, Mountain View, CA",
  "lat": 37.4224,
  "lng": -122.0840,
  "zoom": 18,
  "detections": [
    { "class": "building", "confidence": 0.92, "bbox": [102, 84, 310, 275] },
    { "class": "vehicle",  "confidence": 0.87, "bbox": [45,  190, 88,  224] },
    { "class": "vehicle",  "confidence": 0.81, "bbox": [200, 300, 245, 335] }
  ],
  "detection_count": 3,
  "processing_time_ms": 1847,
  "imagery_source": "google_maps_static"
}

Rate Limits by Pricing Tier

Searches reset on the 1st of each month. There are no overage charges — once the monthly limit is reached, the account is prompted to upgrade to the next tier.

Frequently Asked Questions

Conclusion

A satellite building detection API eliminates the cost and complexity barrier that has historically kept aerial intelligence tools out of reach for mid-market insurance, construction, and real estate companies. The key criteria for evaluating providers are accuracy (published mAP), latency (real-time vs. batch), pricing transparency, and the ability to operate without proprietary satellite hardware.

Kestrel AI checks all four boxes: 88.7% mAP, sub-3-second latency, public pricing starting at $99/month, and zero hardware dependency. For teams priced out of Maxar and Planet Labs, it's the production-ready alternative.