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.
