Abstract

Wildfires are some of the most volatile and destructive natural calamities, and they have a tremendous potential to endanger lives, property, and the environment. Wildfire Analysis Drone or W.A.D is an intelligent surveillance system that is meant to improve wildfire detection, tracking, and response through the collection of real-time data and remote reporting. W.A.D combines the essential sensors such as gas, flame, and temperature sensors with a GPS module and a SIM-based communication mechanism. When it detects possible fire incidents, the system instantly sends the location and corresponding sensor readings to firefighting officials, allowing quicker response times and better situational awareness. Through the automation of environmental monitoring in areas susceptible to wildfires, W.A.D. minimizes the threats to human responders and offers valuable information for proactive disaster management. This article describes the system architecture, sensor integration, communication process, and field test outcomes, which prove the prototype to be an easily scalable and low-cost solution for early

Introduction

Wildfires ignite suddenly and spread rapidly, destroying forests, wildlife habitats, homes, and endangering human lives. Traditional detection techniques—satellite imagery, watchtowers, and patrols—are often too slow to identify fires at their earliest stage. To address this delay, the Wildfire Analysis Drone (W.A.D.) was developed as a fast, intelligent early-warning system designed to detect fire indicators before they escalate into major disasters.

Overview of W.A.D

W.A.D is a lightweight drone equipped with gas, flame, smoke, and temperature sensors, a GPS module, and a SIM-based communication system. It continuously scans wildfire-prone areas and instantly sends real-time alerts—including sensor readings and GPS coordinates—to firefighters. By flying into dangerous or hard-to-access zones, it reduces the need for firefighters to perform risky initial assessments.

W.A.D’s core purpose is to provide early detection, accurate hotspot mapping, and safer decision-making for emergency responders.

LITERATURE REVIEW SUMMARY

Recent research strongly supports the use of UAVs for wildfire monitoring:

• PULSAR (2024) introduced a reconfigurable drone structure using edge computing for low-latency wildfire surveillance.

• Hodgson & Baylis (2016) verified drones' usefulness for environmental monitoring in remote areas.

• Xiwen Chen et al. (2022) developed an RGB + thermal imaging dataset that improved deep-learning wildfire detection accuracy.

• Afghah & Razi (2019) proposed autonomous multi-drone networks with coordinated routing for large-area coverage.

• Muksimova & Umirzakova (2024) achieved efficient fire recognition using a modified transformer model suitable for low-power drone hardware.

These studies highlight the need for thermal imaging, multi-sensor fusion, AI-based detection, and robust real-time communication—all of which W.A.D aims to integrate.

METHODOLOGY SUMMARY

1. Research Approach

A mixed-method approach was used—studying firefighter challenges (qualitative) and evaluating drone components and AI models (quantitative).

2. Problem Identification

Firefighters often lack real-time knowledge of:

• fire spread,

• temperature hotspots,

• smoke levels,

• and terrain obstacles.
  W.A.D was conceptualized to fill this critical information gap.

3. System Architecture

The system has three main units:

1. Drone Unit – gathers multi-sensor data while flying autonomously or manually.

2. Data Processing Pipeline – processes sensor readings via onboard or remote computing.

3. Command Centre Interface – displays analyzed data for firefighter decision-making.

4. Design Considerations

• Lightweight sensor integration

• Low-latency processing through edge computing

• Heat-resistant, stable drone structure

• FAA compliance with manual override

• Scalable modular design

5. Data Flow

1. Acquisition – sensors detect temperature, smoke, gas levels.

2. Processing – onboard or external analysis.

3. Transmission – SIM module sends data to command centre.

4. Visualization – dashboard displays hotspots, coordinates, and risk levels.

6. Development Phases

Requirements → Design → Prototype → Testing → Deployment → Evaluation.

RESULTS SUMMARY

Prototype Version 1 successfully demonstrated:

• Detection of flames, smoke, and gas

• Identification of danger zones through temperature thresholds

• Accurate hotspot localization via GPS

• Real-time transmission to the command centre

Though functional, further calibration, stability improvements, and ruggedization are needed for real wildfire deployment.

CHALLENGES

Major issues encountered:

• Sensor calibration inconsistencies in heat and smoke

• High power consumption reducing flight duration

• Processing delays during real-time recognition

• Drone instability in windy wildfire environments

• Regulatory restrictions requiring fail-safe mechanisms

FUTURE SCOPE

W.A.D can be expanded to:

• Drone swarms for large-area wildfire coverage

• Integration with satellites and geospatial analytics

• Predictive fire modelling using weather and vegetation data

• Onboard AI for faster decision-making

• Adaptation for other disasters (floods, volcanic gases, industrial leaks)

Overall, intelligent drones like W.A.D can transform wildfire response by providing early detection, reducing human risk, and enabling smarter emergency planning.

Conclusion

The development of the Wildfire Analysis Drone marks a significant advancement in the approach to managing environmental disasters. By fusing artificial intelligence with unmanned aerial technology, the project effectively addresses the critical need for safer, faster, and more informed firefighting strategies. It enables emergency responders to obtain crucial data without exposing themselves to direct risk. Through autonomous navigation and sensor-based evaluation, W.A.D. delivers real-time feedback, thus reducing delays in decision-making. This integration not only enhances operational coordination but also mitigates property damage and potential loss of life. Overall, the system’s intelligent design demonstrates how technology can be leveraged for public safety and environmental protection. With continued development, W.A.D. holds the potential to redefine how we prepare for and respond to

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Copyright

Copyright © 2025 Siddhant Udavant, Siddhesh Songare, Ishika Shukla, Shruti Sonwane, Sujeet Solankhe, Arjun Sonkusare. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.