InsurTech / PropTech

Wildfire Risk Assessment

Name: Satalyse
Author: Satalyse

Current wildfire risk, not last year's survey.

0–100 (higher = riskier)

Risk score range

Last 30 days of imagery

Data currency

30 m (SRTM)

Terrain resolution

100 m (Landsat thermal)

Temperature resolution

The Problem

Property insurers, developers, and municipalities need current wildfire hazard assessments, but fire risk maps published by government agencies update annually at best, using models that don't reflect recent vegetation change, drought stress, or fuel accumulation. Post-wildfire insurance withdrawals from entire counties have created coverage gaps because risk signals arrived too late for proactive underwriting adjustments.

How Satalyse Solves It

Satalyse's Risk Scoring endpoint calculates wildfire risk by combining live satellite-derived vegetation dryness (from Sentinel-2 NDVI and moisture index), surface temperature from Landsat thermal bands, and terrain slope from SRTM, building a composite hazard score calibrated to current conditions, not last year's model. Feature Extraction can be run first to get granular fuel-load metrics for high-precision applications.

Risk Scoring

POST /api/v1/satellite/score-risk

Composite wildfire risk score using current vegetation dryness, temperature, and terrain

Feature Extraction

POST /api/v1/satellite/extract-features

Returns granular vegetation dryness, fuel moisture index, and surface temperature

Code Example

wildfire-risk-assessment.py

REQUEST

1"color:#c084fc">import requests
2
3"color:#4b5563"># Assess wildfire risk "color:#c084fc">for a hillside residential development "color:#c084fc">in Cali"color:#c084fc">fornia
4response = requests.post(
5    "https://api.satalyse.dev/api/v1/satellite/score-risk",
6    headers={"Authorization": "Bearer sat_sk_live_..."},
7    json={
8        "geometry": {
9            "type": "Polygon",
10            "coord">inates": [[
11                [-118.731, 34.172], [-118.712, 34.172],
12                [-118.712, 34.158], [-118.731, 34.158],
13                [-118.731, 34.172]
14            ]]
15        },
16        "asset_type": "residential",
17        "location_context": "rural",
18        "risk_factors": ["wildfire"],
19        "time_horizon_days": 365
20    }
21)
22
23data = response.json()
24fire = data["component_scores"]["wildfire"]
25"color:#c084fc">print(f"Wildfire score:  {fire['score']}/100")
26"color:#c084fc">print(f"Risk level:      {fire['level']}")
27"color:#c084fc">print(f"Premium impact:  {data['">insurance_implications']['estimated_annual_premium_multiplier']}x")

200 OKRESPONSE

{
  "request_id": "req_fire001",
  "status": "success",
  "timestamp": "2025-01-15T12:00:00Z",
  "location": { "area_km2": 0.30, "asset_type": "residential" },
  "overall_risk_score": 82,
  "overall_risk_level": "very_high",
  "overall_risk_trend": "increasing",
  "component_scores": {
    "wildfire": { "score": 82, "level": "very_high", "confidence": 0.80 }
  },
  "risk_summary": {
    "primary_risks": [
      { "risk": "wildfire", "score": 82, "action": "Create defensible space and clear dry vegetation" }
    ]
  },
  "insurance_implications": {
    "estimated_annual_premium_multiplier": 1.41,
    "recommendation": "Enhanced wildfire coverage required"
  },
  "recommendations": [
    { "priority": "high", "action": "Clear dry vegetation within 30m perimeter", "estimated_cost": "low", "risk_reduction": 20 }
  ],
  "api_metadata": { "version": "2.0", "execution_time_ms": 3900, "cost_credits": 40, "cache_hit": false }
}