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Wildfire

Early wildfire detection (minutes vs hours) saves lives and reduces suppression costs by orders of magnitude.

Sensor: thermal IR (MWIR/LWIR bands).

Pipeline:

  • Collect: thermal imagery over monitored fire-risk zones.
  • Map: for each pixel, compute brightness temperature. Flag pixels where T>T > contextual threshold (accounting for solar heating, land cover). Compare against a running background model.
  • Reduce: cluster adjacent hot pixels. Filter false positives (sun glint, industrial heat). Generate alert if cluster exceeds minimum size and persistence (confirmed on 2+ consecutive frames if available).

Alert payload: ~1 KB (coordinates, temperature, cluster size, confidence, timestamp).

Feasibility:

  • Thermal anomaly detection is well-understood (MODIS, VIIRS algorithms exist).
  • Computational cost is low (thresholding + clustering).
  • Time-critical: minutes matter. Onboard processing eliminates the ground-processing delay entirely.
  • False positive rate is the main challenge; contextual algorithms help but are more compute-intensive.