# Prediction Market Architecture for Disasters

> Prediction markets for natural disasters require real-time data pipelines combining satellite imagery, IoT sensors, and probabilistic models to produce accurate odds within minutes.

**URL:** https://www.ciptadusa.com/blog/prediction-market-disaster-architecture-20260706  
**Type:** blog  
**Author:** PT Cipta Dua Saudara  
**Category:** Engineering  
**Published:** 2026-07-06  
**Cover:** https://cdn-uagents.enitip.com/uploads/blog/2026-07/daily-engineering-20260706-014533.jpg  

## Article

Polymarket and similar platforms now let you bet on whether a wildfire will reach your neighborhood. Behind that simple interface lies an engineering system that must process real-time geospatial data, compute dynamic probabilities, and auto-resolve contracts — an architecture challenge rarely discussed in public.

## Summary

Prediction markets for natural disasters require real-time data pipelines combining satellite imagery, IoT sensors, and probabilistic models to produce accurate odds within minutes of new fire detection.

## Background

Traditional prediction markets — from Intrade to Polymarket — were designed for discrete events: elections, sports matches, product launches. Contract resolution is binary and timelines are clear. When these platforms began offering wildfire contracts, the old architecture fell short.

The problem is fundamental: a wildfire is not a discrete event. Fire moves, changes direction with wind, and defining "burn down your town" requires precision geofencing. The system must answer hard technical questions — when does a contract resolve, what data serves as the oracle, and how do you prevent manipulation.

## Approach

The architecture for geospatial continuous-event prediction markets requires three layers working in concert:

**1. Data ingestion pipeline** — Sources include NASA's FIRMS (Fire Information for Resource Management System) updating hotspots every 3 hours, local weather sensors for wind speed and direction, and Sentinel-2 satellite imagery at 10-meter resolution. The pipeline must normalize these heterogeneous formats into a unified event stream with sub-minute latency.

**2. Probabilistic resolution engine** — Unlike simple oracles that read election results, the resolution engine here must define boundaries programmatically. The approach uses Uber's H3 hexagonal grid for consistent geofencing, VIIRS thermal anomaly thresholds as triggers, and a 72-hour grace period after last-hotspot for final resolution.

**3. Automated market maker** — A Logarithmic Market Scoring Rule (LMSR) based AMM adjusts odds based on incoming data streams. Every FIRMS update shifts the probability surface across all active contracts within 50 km of a new hotspot.

The hardest engineering challenge isn't the trading engine — that's a solved problem. It's oracle design: how to define "burn down" deterministically when reality is continuous and ambiguous.

## References

- [Prediction Markets Let You Bet on Whether a Wildfire Will Burn Down Your Town](https://www.wired.com/story/prediction-markets-let-you-bet-wildfire/) — Wired
- [NASA FIRMS Active Fire Data](https://firms.modaps.eosdis.nasa.gov/) — NASA EOSDIS
- [Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation](https://mason.gmu.edu/~rhanson/mktscore.pdf) — Robin Hanson

---

*Markdown version of https://www.ciptadusa.com/blog/prediction-market-disaster-architecture-20260706 — generated for AI agents and LLM crawlers.*