AI-Based Detection and Classification of Industrial Fires and Persistent Thermal Sources Using NASA FIRMS, OSM & Satellite Data
“Satellite systems like NASA FIRMS detect heat spots on Earth but cannot tell if they are industrial plants, gas flares, or forest fires. We need to build an intelligent map system that combines satellite heat data, land maps, and industrial records to automatically spot, classify, and track industrial fires and persistent thermal sources.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An AI-enabled geospatial intelligence platform that ingests live satellite thermal anomalies, correlates them with industrial infrastructure databases (like OSM and plant registries), and automatically classifies thermal sources (industrial fires, gas flares, mining, vs natural wildfires) on an interactive GIS map dashboard.
WHO IS IT FOR?
National Technical Research Organisation (NTRO), disaster management authorities, industrial safety regulators, and national security agencies needing real-time monitoring of critical infrastructure hazards.
WHY DOES IT EXIST?
Current satellite fire alerts are noisy and trigger false alarms because raw thermal pixels cannot differentiate between routine industrial gas flares, active steel plants, forest fires, and accidental refinery explosions, delaying emergency response.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
NASA FIRMS CSV/API feeds (MODIS/VIIRS), OpenStreetMap (OSM) spatial polygons of industrial zones, Sentinel-2/Landsat multispectral satellite imagery, and static industrial registry databases (coordinates of refineries, power plants, LNG terminals).
Spatial cross-referencing of thermal hotspots with known industrial footprints using PostGIS, multi-spectral image feature extraction (NDVI/NDWI indices), temporal persistence analysis, and deep learning classification to separate flare signatures from accidental fires.
An interactive Web-GIS dashboard displaying classified thermal anomalies with color-coded markers, automated severity scoring, time-series anomaly trend graphs, and instant SMS/Web-hook alerts sent to authorities when an anomaly deviates from baseline industrial behavior.
CONSTRAINTS
- Must handle low-resolution thermal pixels (MODIS is ~1km/pixel) requiring sub-pixel estimation or multi-sensor fusion.
- Satellite data latency and intermittent API rate limits.
- Must operate efficiently on web browsers with smooth rendering of thousands of vector polygons using vector tiles.
JURY METRICS
- Classification accuracy (Precision/Recall in separating industrial fires from gas flares and wildfires).
- False alarm rate reduction percentage compared to raw NASA FIRMS alerts.
- GIS rendering performance (FPS and load times with thousands of active global thermal points).
CORE BOTTLENECK
Distinguishing between high-temperature persistent industrial operations (like routine gas flares or slag dumps) and sudden accidental industrial fires using coarse spatial resolution satellite data.
Why You Should Choose This PS (Pros)
- High national security and strategic relevance for NTRO, making it a priority sector for judges.
- Clear integration pipeline using well-documented open data sources (NASA FIRMS, OSM, Geoserver).
- Strong visual appeal with map overlays and real-time dashboard alerts.
Challenges & Risks to Watch (Cons)
- Dealing with coarse satellite resolutions can lead to edge-case classification errors.
- Requires robust spatial database indexing (PostGIS) which can be tricky to configure under tight deadlines.
- Crowded disaster management domain if teams only build a basic map wrapper.
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
Current satellite-based fire monitoring systems such as NASA FIRMS provide thermal anomaly detections but do not distinguish between industrial fires, gas flares, agricultural burning, mining activity, and wildfires.
The challenge is to develop an AI-enabled geospatial system that can automatically identify, classify, and monitor industrial fires and persistent thermal sources by integrating thermal anomaly data, land-cover information, industrial infrastructure databases, and satellite imagery.