Signet – Autonomous wildfire tracking from satellite and weather data
I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking. All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats. Most of the system is deterministic plumbing - ingestion, spatial…
Signet – Autonomous wildfire tracking from satellite and weather data launched on March 15, 2026 with 123 votes, #328 of 23,380 launches that month and more than 95% of that year's launches.
In plain words
Signet is an autonomous wildfire monitoring system built in Go that automatically processes satellite thermal detections, weather forecasts, terrain data, and fuel models to identify and track wildfires. It integrates data from NASA, NOAA, USGS, and other sources to automate the manual monitoring work currently done by hand, using deterministic data processing for routine tasks and AI-assisted analysis to evaluate weak detections and prioritize which fires warrant investigation.
written from the facts on this page · September 2026
From the sources
Signet is an autonomous wildfire intelligence agent that monitors the continental US 24/7. It combines NASA satellite data, NOAA imagery and weather, fuel models, terrain, and exposure data to track incidents, generate outlooks, and predict fire behavior. The agent decides what to investigate, makes predictions, and verifies them against the next satellite pass. Sign up by ZIP code for free alerts when notable fire activity is detected nearby.
Autonomous wildfire tracking from satellite detections, thermal imagery, and weather data across the continental US.
Signet turns satellite detections, thermal imagery, and weather data into autonomous wildfire tracking across the continental US. Continuously triages NASA FIRMS detections, GOES-18/19 imagery, and follow-up investigations without waiting for a human to initiate each step. Each cycle returns both an assessment and the next-cycle decision, while incidents, observations, predictions, and agent decisions are logged separately. Correlates satellite thermal imagery with geographic and environmental data to analyze spatial patterns and temporal evolution. Uses data from NWS, USGS, LANDFIRE, Census, OpenStreetMap, and grounded web context to evaluate fire behavior, nearby exposure, and likely…from signet.watch
In the maker’s words, at launch
I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking. All the data already exists: NASA FIRMS thermal…
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