Identifying Tree to Prevent Forest Fires Using AI

Background

Deforestation, climate change, and the rising risk of wildfires present critical global challenges. Wildfires release approximately 8 billion tons of CO2 into the atmosphere every year, according to Greenpeace—half the annual emissions from global coal burning. The drying of vegetation due to rising temperatures further exacerbates these risks, endangering ecosystems, infrastructure, and human lives.

To address these issues, Swedish AI startup Spacept collaborated with Omdena to develop an AI-powered solution to prevent forest fires. By identifying trees close to power infrastructure, the project helps mitigate the risks of power outages and wildfires while contributing to global efforts to reduce CO2 emissions.

Objective

The project aimed to create a deep learning model that could accurately identify trees posing a risk to power stations. This model would enable proactive measures to prevent forest fires and power outages, while also drastically reducing the time and costs associated with traditional vegetation inspections.

Approach

The collaboration brought together 36 AI engineers, data scientists, and enthusiasts to build a robust solution to prevent forest fires:

1. Data Labeling and Augmentation:
   Collaborators labeled satellite images and applied advanced techniques like Generative Adversarial Networks (GANs) to augment data, enhancing the model’s learning capabilities.
2. Model Development:
   A deep U-Net model was developed, achieving a 95% accuracy rate in detecting trees near power lines. This model combined machine learning with human judgment for optimal performance.
3. Additional Features:
   A task group created an elevation map to visualize forest cover, further improving the solution’s utility.
4. Integration:
   The resulting model was integrated into Spacept’s AI-powered product, which combines satellite imagery with machine learning to detect risks posed by vegetation and extreme weather.

Results and Impact

This project demonstrated how AI can play a crucial role in efforts to prevent forest fires and protect infrastructure:

• High Accuracy: The deep learning model achieved 95% accuracy in identifying high-risk trees.
• Cost and Time Efficiency: The AI-powered system reduced the need for manual inspections, saving resources and enabling real-time risk management.
• Environmental Benefits: By helping to prevent forest fires, the solution supports CO2 reduction efforts and safeguards biodiversity.
• Global Applications: Insights from this project are now aiding another initiative with Sintecsys, a Brazilian company, to detect and prevent fires in the Amazon rainforest in near real-time.

Future Implications

The success of this project highlights the transformative potential of AI in efforts to prevent forest fires and mitigate the effects of climate change. Potential future applications include:

• Scaling Worldwide: Expanding the solution to wildfire-prone regions such as California, Australia, and parts of Europe.
• Policy Development: Supporting governments and utility companies in enforcing vegetation management regulations to minimize fire risks.
• Climate Change Adaptation: Leveraging AI to monitor and protect critical ecosystems while preparing for the increasing impacts of extreme weather events.

By combining satellite imagery and machine learning, the collaboration between Omdena and Spacept demonstrates how technology can be harnessed to effectively prevent forest fires, save lives, and protect our planet.