open source · MIT licence

Detect wildfires
before they spread.

AI-powered satellite monitoring for Indonesia. Ingests NASA FIRMS data, runs Isolation Forest anomaly detection across an H3 hexagonal spatial grid, and delivers ranked daily alerts.

$ git clone https://github.com/Itsavirus-com/anomalous-wildfire-hotspots-detection
view live dashboard star on GitHub
MIT
Python 3.10+ · FastAPI · Next.js 14
Nov 2025 — present
21M+
hectares burned annually across Indonesian forests and peatlands
100K+
hotspots detected per year via NASA FIRMS instruments
<1hr
detection latency from capture to alert delivery
// pipeline
01 — data ingestion
NASA FIRMS pull
VIIRS and MODIS data ingested daily into PostgreSQL/PostGIS.
VIIRS · MODIS · PostgreSQL
02 — spatial aggregation
H3 hexagonal grid
Hotspots aggregated into H3 cells for consistent geospatial analysis.
H3 · PostGIS · Pandas
03 — anomaly detection
Isolation Forest ML
Unsupervised model flags abnormal spatial-temporal hotspot patterns.
Scikit-learn · NumPy
04 — alert generation
Ranked daily alerts
Top-K alerts ranked by anomaly severity with coherence checks.
Top-K scoring · validation
// capabilities
Interactive H3 map
Hex grid with anomaly severity visualization.
ML anomaly detection
Isolation Forest model without static thresholds.
Smart alert system
Daily top-K ranking with severity classes.
NASA FIRMS integration
Automated pull from VIIRS and MODIS feeds.
Time-series analysis
Rolling-window hotspot trend and peak analysis.
Pipeline monitoring
Operational status tracking and manual triggers.
// stack
backend
FastAPIPostgreSQLPostGISScikit-learnH3 SpatialSQLAlchemyPandasNumPy
frontend
Next.js 14TypeScriptDeck.glMapLibre GLTailwind CSSRechartsSWRZustand
// how the technology works
01 — data source
NASA FIRMS Satellite Feed
Near real-time fire detections from VIIRS and MODIS are ingested daily and used as model input features.
02 — spatial indexing
H3 Hexagonal Grid
Coordinates are mapped to H3 cells, enabling uniform neighbor analysis and robust cross-island comparisons.
03 — anomaly detection
Isolation Forest
The model learns baseline behavior and isolates statistically unusual cell-days for further ranking.
04 — alert ranking
Hybrid Scoring + Spatial Coherence
Final ranking combines anomaly score and neighboring-cell coherence to reduce isolated false positives.
// real data — Indonesia deployment
11,867
Raw hotspots
FIRMS records ingested from VIIRS and MODIS satellites.
VIIRS · MODIS
7,765
Cell-day aggregates
H3 cells grouped by date for regional analysis.
H3 grid
752
Anomalies detected
Cell-days flagged anomalous by Isolation Forest.
ML model
649
Alerts generated
Daily top-K alerts after coherence validation.
alert engine

Deploy it yourself.

Self-host the full stack. MIT license. Built with NASA FIRMS data for Indonesia and adaptable anywhere.

view live dashboard view on GitHub read the docs