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Adoption of AI, Cloud for Disaster Response and Mitigation in the U.S.

Researchers at Pacific Northwest National Laboratory (PNNL) utilise technology to lessen the impact of natural disasters. They are expanding PNNL’s operational Rapid Analytics for Disaster Response (RADR) image analytics and modelling suite to mitigate damage to key energy infrastructure. PNNL collaborates with the Department of Defense’s Joint Artificial Intelligence Center, and the Department of Homeland Security.

By using a combination of image capturing technology such as satellite, airborne, and drone images, artificial intelligence (AI), and cloud computing, the team works to assess as well as predict the damage. Accurately forecasting the movement of natural disasters such as wildfires, floods, hurricanes, windstorms, tornados, and earthquakes allows the responders to take measures to reduce damage, conduct advanced resource planning, and increase infrastructure restoration time.

The U.S. Department of Energy (DOE) Artificial Intelligence and Technology Office stated that the effort to apply AI is exciting and timely as it reduces the impact of wildfires, protects energy infrastructure, and ultimately saves lives. The work has the potential to make a difference in a very challenging wildfire season.

The project originally started with the creation of a change-detection algorithm, which analyses different types of satellite imagery and determines what changed in the landscape after a storm. Authorities use the tool to rapidly assess the physical damage impact of natural disasters, often before ground teams can get in.

To improve the timeliness and ground-level assessments, the team incorporated new and different image sources. RADR can pull in images from a variety of satellites with different sensing capabilities, including domestic and international government satellites that are offered as open data as well as commercial satellites that are available through the International Disasters Charter.

Having multiple sources of overhead imagery improves response time to just a few hours with the key limitation being the latency of overhead imagery, or the time between images being collected and being available for analysis. Once imagery is received, the RADR software can generate analysis in just over 10 minutes.

The team is also integrating publicly available and crowdsourced images from social media. By pairing overhead imagery with on-the-ground images, the team can provide a more complete assessment. Satellite images, for example, may show damage to a generation resource, power lines, or the electric grid; however, ground images may indicate otherwise. The tool takes all these images, removes the redundant ones, and sews the images together to provide a more accurate view of changing conditions.

The added imagery sources provide additional data for RADR to interpret, improving accuracy. To predict possible outcomes of a wildfire, the team is combining the imagery analysis with the weather, fuel and forecast data. For example, wind, vegetation, and anything a fire can consume all factor into the size of a fire and the direction it takes. By marrying imagery with fuel data and wildfire models, the team hopes to be able to accurately predict the path a fire takes.

Coordinating a response requires local, regional, and national resources, each in different locations but needing the data as quickly as possible in a format that can be readily accessed and interpreted, particularly in a data communication constrained environment. A cloud-based system provides an end-to-end pipeline for retrieving available imagery, processing the analytics, and disseminating data to be used directly in a user’s own software, through desktop web browsers, and/or via mobile applications. Added visual analytics produce images and datasets that can be easily discernable to a wide audience of responders.

Recent years have brought an increase in the frequency and severity of wildfires, floods, and other extreme weather events. The researchers hope that the added capabilities of RADR will give responders information that can be used to make informed decisions, reduce or plan for damage to key energy infrastructure, plan relief efforts, and save lives.

As reported by OpenGov Asia, U.S. Researchers have been utilising the power of AI to mitigate disasters such as the adoption of AI-the powered tool in firefighting. The tool was designed to predict and warn of a deadly phenomenon in burning buildings known as flashover, when flammable materials in a room ignite almost simultaneously, producing a blaze only limited in size by available oxygen. The tool’s predictions are based on temperature data from a building’s heat detectors, and the AI is designed to operate even after heat detectors begin to fail.

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