Luigi La Spada¹, Nida Zeeshan¹, Makhabbat Bakyt², Kazybek bi Zhanibek³ and Saya Santeyeva², ¹Edinburgh Napier University, United Kingdom, ²Gumilyov Eurasian National University, Kazakhstan, ³Almaty University, Kazakhstan

Presented is an advanced geoinformation system for monitoring and forecasting forest fires, utilizing unmanned aerial vehicles (UAVs) and a novel lightweight neural network-based encryption technique. The system incorporates an innovative aerospace data processing algorithm that achieves a fire detection accuracy of 98.7% and forecasts fire spread with an average prediction error of 12.5 m and a maximum error of 28.5 m. Notably, the proposed encryption method secures data transmission from the UAV to the ground station and operates 20% faster than the conventional AES-128 standard. Experimental results validate the system's capability to accurately detect fire incidents, efficiently predict their spread, and reliably safeguard transmitted information. Although effective in monitoring extensive forest areas and facilitating prompt emergency responses, its accuracy is somewhat constrained by factors such as UAV altitude and image resolution. Future research will aim to develop adaptive UAV control strategies and incorporate multi-sensor fusion techniques to further enhance performance.

Forest Fires, UAV, Geographical Information System, Neural Network, Data Encryption, Aerospace Data, Intelligent Processing.