PropTech / InsurTech

Flood Risk Assessment

Name: Satalyse
Author: Satalyse

Know the flood risk of any location before you underwrite it.

Global (56°S to 60°N)

Coverage

30 m elevation model

Spatial resolution

0–100 (higher = riskier)

Score range

1984–present (JRC)

Historical data depth

The Problem

Property lenders, insurers, and real estate platforms need location-specific flood risk data at point-of-decision, but FEMA flood maps are outdated, cover only the US, and don't account for changing precipitation patterns driven by climate shift. Integrating multiple data sources (elevation, surface water history, upstream drainage) requires specialist GIS teams most organizations don't have.

How Satalyse Solves It

Satalyse's Risk Scoring endpoint combines SRTM elevation data, JRC Global Surface Water historical flood extent, and CHIRPS precipitation records to return a composite flood risk score (0–100), a risk level classification, and confidence-weighted contributing factors, in a single API call for any polygon globally. Integrate into property listing workflows, loan origination systems, or underwriting platforms.

Risk Scoring

POST /api/v1/satellite/score-risk

Returns composite flood risk score with elevation, water history, and drainage factors

Temporal Analysis

POST /api/v1/satellite/analyze-temporal-trends

Shows historical flooding frequency trends for the location over 5–10 years

Code Example

flood-risk-assessment.py

REQUEST

1"color:#c084fc">import requests
2
3"color:#4b5563"># Assess flood risk "color:#c084fc">for a commercial property "color:#c084fc">in Houston
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                [-95.369, 29.762], [-95.355, 29.762],
12                [-95.355, 29.751], [-95.369, 29.751],
13                [-95.369, 29.762]
14            ]]
15        },
16        "asset_type": "commercial_property",
17        "location_context": "urban",
18        "risk_factors": ["flood"],
19        "time_horizon_days": 365
20    }
21)
22
23data = response.json()
24flood = data["component_scores"]["flood"]
25"color:#c084fc">print(f"Flood risk score: {flood['score']}/100")
26"color:#c084fc">print(f"Risk level: {flood['level']}")

200 OKRESPONSE

{
  "request_id": "req_flood001",
  "status": "success",
  "timestamp": "2025-01-15T12:00:00Z",
  "location": { "area_km2": 0.15, "asset_type": "commercial_property" },
  "overall_risk_score": 71,
  "overall_risk_level": "high",
  "overall_risk_trend": "increasing",
  "component_scores": {
    "flood": { "score": 71, "level": "high", "confidence": 0.85 }
  },
  "risk_summary": {
    "primary_risks": [
      { "risk": "flood", "score": 71, "action": "Evaluate drainage infrastructure" }
    ]
  },
  "insurance_implications": {
    "estimated_annual_premium_multiplier": 1.36,
    "recommendation": "Enhanced flood coverage recommended"
  },
  "recommendations": [
    { "priority": "high", "action": "Install flood barriers", "estimated_cost": "medium", "risk_reduction": 25 }
  ],
  "api_metadata": { "version": "2.0", "execution_time_ms": 3800, "cost_credits": 40, "cache_hit": false }
}