AI for the environment - Wildlands
Delivering AI-backed tools that improve the health and resilience of our wildlands by supporting fire management.
All over the world, the health of our forests impacts the quality of the air we breathe and the water we drink. Effective management of burnable material in forests is essential to support healthy, resilient forests that are less susceptible to catastrophic wildfires.
Our approach
At Ai2, a non-profit based in Seattle, the Wildlands team is working to apply machine learning to support wildland management. We are focusing our work on ground-level fuels that are not measurable with current satellite technology. Surface fuels are a neglected research area in wildfire research and management – we are taking current low-tech methods and applying computer vision to amplify impact.
What is fuel management?
Anything burnable is considered fuel in a forest ecosystem. When fuels are in excess and conditions are right, fires can burn hotter, longer, and faster than expected, making them more challenging to manage.
Fire plays an important role in minimizing the negative impacts on forests, and not all fire is bad. Prescribed burning can help reduce fuels to help improve habitats and maintain ecosystems. By managing fuels, we can prevent fires from growing out of control, helping to save lives and property.
Fuel loading estimates for fire-prone ecosystems are vital for accurately predicting fire behavior and effects, including flame length and intensity, as well as smoke emissions, soil heating, nutrient dynamics, and water filtration rates.
Improving current techniques
Photoloading is a technique for estimating the fuel loading of a forest floor by visually comparing conditions captured in the field against a set of standards, or sequences. Photoload sequences are downward-looking, close-up photographs showing graduate fuel loadings of synthetic fuel beds.
While the photoload technique is relatively accurate and inexpensive, it is still a time-consuming process that is not widely adopted. With the help of machine learning, we hope to make this process faster and easier, greatly increasing the number of measurements available to help support fuel analysis and fire management.
AI to estimate fuels
The Wildlands team is working on training an AI model to estimate the loading for 1, 10, and 100-hour fuels in Kg/m2 using photos taken with a digital camera or cell phone. This project will enable any forester with a camera to report quick, accurate fuel loading estimates, allowing fire managers, fuel specialists, and researchers to make more accurate planning predictions. This is part of a longer-term vision to apply machine learning to support many aspects of wildland fire management.
Fuels Data app – data collection streamlined
Fuels Data is designed for wildland management practitioners tasked with collecting surface fuels measurements in the field. It automatically performs calculations and table lookups. Photos and data are linked together, which eliminates the need to diligently sequence reference photos to align with data. These features reduce the risk of mistakes in transcribing and reviewing collected data in the field. The collected data may also be uploaded to the Ai2 to build and train fuel load estimation models.
The application addresses three major needs:
- Eases the work of practitioners collecting data in the field
- Provides tools to help summarize the data collected
- Constructs a dataset suitable for training AI models tasked with assisting practitioners in the future
FAQs - Popular questions
We are a team of machine learning researchers, engineers, and designers at the Allen Institute for Artificial Intelligence (Ai2) based in Seattle, WA. Ai2 is a non-profit focused on using Artificial Intelligence to solve toughest problems and give it away for the common good. This project will enable any forester with a camera to report quick, accurate fuel loading estimates, allowing fire managers, fuel specialists, and researchers to make more accurate planning predictions. This is part of a longer-term vision to apply machine learning to support many aspects of wildland fire management.
To learn more about photoloading and how the Fuels Data app has the potential to make this work easier, check out our blog posts.
It is a common identifier generated in the application.The identifier is automatically generated using the application and is a series of three words, it is shared with folks helping collect data, who will enter these three words on their device inside the Fuels Data app. That identifier will be associated with all data collected for that project and can be used to request that AI2 provide the aggregated data after users have uploaded from their devices.
You can request your data through the app by selecting the “Request Data Summary” button on the project details screen. We will need the link code in order to aggregate all the data associated with a project.
We are in the process of collecting as much data as possible, after we have enough data we can start training our models.
You can get updates about new features and releases by subscribing to our newsletter at the bottom of this page. Go here to subscribe to the Wildlands newsletter.
Our work is proceeding in collaboration with the University of Idaho, and the Idaho Prescribed Fire Council. Interested in collaborating? Get in touch.
Send us an email if you:
- Have ideas about how AI can improve forest health
- Are actively doing land management
- Want to incorporate Fuels Data app in your workflow