Flood
Standing water, submerged infrastructure, overflowing rivers.
Class accuracy 96.2%
Active Operations: 14 zones monitored
Last sync 00:00:00 UTC System Online · v2.4 · 4 Disaster Classes
SEE THE
CRISIS.
RANK IT.
ACT.
Real-time AI classification of disaster imagery. Upload photographs from affected areas — get instant severity scoring and dispatch-ready prioritisation in under 3 seconds.
2.4s
Avg. classification
94.7%
Model accuracy
4
Disaster classes
classification_log.stream
● LIVE
FLOOD · 96%
DEBRIS · 78%
FIELD PHOTOGRAPH · 1024×768
Classification FLOOD · CRITICAL
Severity9.2/10
Confidence96.4%
RegionSouth Asia
Latency1.8s
Deployed across humanitarian response networks
UN-OCHA · IFRC · WFP · UNICEF · OXFAM · Red Cross · GDACS · ReliefWeb
// 01 — CAPABILITIES
Built for the
field. Trusted
at scale.
From a single image to thousands per hour — SENTINEL's inference pipeline returns structured, dispatch-ready classifications with sub-3-second latency. No model expertise required from your field team.
Severity scoring, not just labels
Every image receives a 1–10 severity index combining class confidence, damage extent heuristics, and humanitarian impact weighting.
Real-time inference
Edge-accelerated pipeline returns results in 1.8s median. Bulk upload handles 10,000+ images per hour.
Geo-aware
EXIF + reverse geocoding tags every image to admin region.
Encrypted
AES-256 at rest, TLS 1.3 in transit, GDPR compliant.
Multilingual field reports
Auto-generates structured incident reports in 14 languages with dispatch recommendations.
Historical pattern analysis
Aggregate classified imagery reveals seasonality, recurrence, and emerging risk corridors — feed directly into preparedness budgets.
// 02 — CLASSIFIER
Four disaster classes.
One decisive output.
sentinel.classifier · v2.4.1
● READY
01 · INPUT
02 · INFERENCE OUTPUT
AWAITING INPUT
Upload a disaster photograph to receive class, confidence, and severity ranking.
SENTINEL v2.4 is fine-tuned on 487,000 verified disaster images from open humanitarian datasets and field archives. Every prediction returns a class, confidence, and severity score your operations team can act on.
Model metadata
- Architecture
- EfficientNet-B7 (transfer)
- Training set
- 487K images
- Validation acc.
- 94.7%
- Inference time
- 1.8s median
- Last retrained
- 2024-11-18
Landslide
Mud flows, slope collapse, displaced earth, blocked roads.
Class accuracy 93.4%
Earthquake
Collapsed buildings, cracked infrastructure, rubble fields.
Class accuracy 95.1%
Wildfire
Active flames, smoke plumes, scorched vegetation zones.
Class accuracy 94.0%
// 03 — OPERATIONS
From field photograph to dispatch priority in 3 steps.
01 / STEP
Capture
Relief workers photograph the affected zone — phone, drone, or satellite feed. EXIF metadata is preserved for geographic context.
02 / STEP
Classify
SENTINEL's vision model assigns a disaster class, confidence score, and 1–10 severity index. The system flags critical zones automatically.
03 / STEP
Dispatch
Severity-ranked image feed reaches operations centres in under 3 seconds — food, medical, and shelter teams route to the most urgent zones first.
487K
Images in training set
94.7%
Model accuracy
1.8s
Median inference
14
Languages supported
// 04 — LONG-TERM
Beyond the
first response.
operations.dashboard · live
STREAMING
Filter:
Every classified image becomes part of a permanent, queryable record. Governments and NGOs use the historical archive to identify recurring risk corridors, allocate preparedness budgets, and model future crisis scenarios.
- ▸ Time-series risk mapping by admin region
- ▸ Seasonal recurrence detection
- ▸ Budget allocation analytics dashboard
- ▸ Pre-deployment forecasting for responders
Sample data output — Q3 incidents
South Asia Flood 9.2 1,247
Southeast Asia Landslide 7.8 412
Mediterranean Wildfire 8.6 891
Andean Earthquake 9.5 203
Central Africa Flood 8.1 634
Every minute matters.
Automate the triage.
Deploy SENTINEL in your operations centre today. Free for verified humanitarian response organisations.