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AI detections

Not every satellite hotspot is a real fire. Deepfire's agentic detection system scores each hotspot cluster on how likely it is to be a real, new fire.

deepfire:ai-detections — hotspot clusters scored by Deepfire’s detection model.

Satellite hotspots include a lot of noise: industrial heat, sun glint, gas flares, agricultural burns and sensor artefacts. Every time a cluster is created or grows, our model looks at the cluster’s hotspots, the satellite imagery around them, and the location’s history, and returns a probability that the cluster is a real fire.

The probability is compared with a threshold calibrated per country. Clusters at or above it are marked is_fire: true and are the ones that trigger detection alerts in the web app.

Clusters that are already linked to a confirmed incident are not scored again: we already know they are real fires.

Attributes

Field Type Notes
cluster_id string (UUID) id of the feature in deepfire:clusters
confidence string LOW (< 0.30), MEDIUM (0.30–0.80) or HIGH (≥ 0.80)
probability number 0–1 model output
threshold number Decision threshold that applied to this cluster’s country
is_fire boolean probability >= threshold
model_version string Version of the model that produced the score
country string ISO 3166-1 alpha-2 code of the cluster
assessed_at string UTC timestamp of the assessment; a cluster is re-scored as new hotspots arrive

Prefer confidence over reading precision into probability: the buckets are what we use in the web app, and they stay stable across model versions.

Examples

High-confidence detections inside a bounding box:

curl -sG -H "Authorization: Bearer $TOKEN" \
  "https://api.deepfire.co/ogc/features/v1/collections/deepfire:ai-detections/items" \
  --data-urlencode "bbox=-125,32,-114,42" \
  --data-urlencode "filter-lang=cql2-text" \
  --data-urlencode "filter=confidence = 'HIGH'" \
  --data-urlencode "limit=50"

Response (truncated):

{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "id": "ai-detections.4b0c…",
      "geometry": { "type": "Point", "coordinates": [-120.6143, 37.2207] },
      "properties": {
        "cluster_id": "65d45ca4-a7b5-4d7e-85f3-516163c2a31d",
        "confidence": "HIGH",
        "probability": 0.93,
        "threshold": 0.5,
        "is_fire": true,
        "model_version": "2026-09-30",
        "country": "US",
        "assessed_at": "2026-10-22T18:42:10Z"
      }
    }
  ],
  "numberReturned": 1
}

All scores for one cluster, to see how the model’s view changed as it grew:

curl -sG -H "Authorization: Bearer $TOKEN" \
  "https://api.deepfire.co/ogc/features/v1/collections/deepfire:ai-detections/items" \
  --data-urlencode "filter-lang=cql2-text" \
  --data-urlencode "filter=cluster_id = '65d45ca4-a7b5-4d7e-85f3-516163c2a31d'"

See AI detections in the web app

Fires flagged by our detection model on satellite data.

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