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STRATUS UAV: Optimizing Prescribed Burning using UAVs and Predictive Modeling

CSEF · 2026 Environmental Engineering (Track 2) (Senior Division)

Naman Chetwani

Overview

Wildfires have escalated into a global environmental catastrophe, and traditional suppression methods are increasingly insufficient. While prescribed burning is a critical mitigation tool, current implementations rely heavily on expensive, slow-moving manned helicopters that cost upwards of $16,000 per day. Furthermore, current ignition placements depend on human intuition, which struggles to accurately account for the chaotic microclimates generated by wildland fires. To address these severe technological and economic bottlenecks, this engineering project introduces the STRATUS system: a low-cost, autonomous Vertical-Takeoff and Landing (VTOL) UAV paired with a predictive modeling engine. The primary objective is to develop a functional prototype capable of replacing high-overhead aerial operations. The airframe is designed for vertical takeoff in confined areas before transitioning to horizontal flight, and is equipped with an autonomous payload system capable of dispensing 50 chemical ignition spheres per mission. Simultaneously, the Strategic Blackline Optimization Network (SBON) is being developed to predict fire-spread microclimates and determine optimal ignition placements. The system targets a minimum flight endurance of 1.2 hours to access remote backcountry zones, decreasing costs by 4x, increasing telemetry range 3x, against closest competitors. The SBON engine achieves 60% accuracy in fire-spread estimates when compared to historical data. Ultimately, STRATUS enables blacklining-on-demand, allowing defensive burns to be safely deployed hours—rather than days—ahead of impending wildfires, offering a highly scalable solution for global wildfire resilience.

Competition history

  • CSEF 2026 Environmental Engineering (Track 2) (Senior Division) · Entry S-12-09

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