The use of Machine Learning and Artificial Intelligence in disaster management
seeks to help in mitigation wildfires cases. The decision to use these technologies
is a move to reduce the devastating effect of wildfires on wildlife and vegetation
The use of Machine Learning and Artificial Intelligence in disaster management seeks
to help in mitigation wildfires cases. The decision to use these technologies is a move
to reduce the devastating effect of wildfires on wildlife and vegetation cover.
Recent reports show that wildfires in the U.S. are severely destructive because of the
rise in global temperatures and changes in weather patterns. Wildfires are natural
occurrences, but they become unpredictable and uncontrollable during hot and dry seasons.
With this regard, researchers in Stanford plan to use machine learning and satellite
imagery to track and predict at-risk areas.
Tests for the susceptibility of forests and scrublands are still conducted manually by
sampling branches and foliage to determine their water content. This method is accurate
and reliable but becomes labor-intensive and unsuitable for scaling.
All hope is not lost because sources of data are becoming more and more available than
before. Sentinel and Landsat satellites are European space agencies that aim to create
an accumulative store for imagery of the Earth’s surface. These satellite images are to
be keenly analyzed to provide a secondary source of information for wildfire risk assessment.
This move intends to reduce splinters and other injuries.
Earlier attempts to implement Earth observation from orbital imagery majorly depended
on visual measurement, which is site-specific. The method of analysis differed based
on the location. The team at Stanford plans to leverage Sentinel satellites’ synthetic
aperture radar that can penetrate forest canopy and take images of the surface.
During a press release, Alexandra Konings said that their latest satellites use longer
wavelengths, which help observations to be sensitive to water in the forest canopy. He
explained that this aims to be a direct representation of the fuel moisture content. Alexandra
Konings is a senior author of the paper, Stanford Ecoydrologist.
The team at Stanford plans to use imagery captured regularly since 2016 and manual
measurements made by the U.S. Forest Service, in a machine learning model. This plan allows
the model to understand the relationship between specific features of the imagery and the
The team intends to test the resulting Artificial Intelligence program to predict old data
for which the answers exist. They plan to give the model up-to-date data to make predictions
about future wildfire seasons. With the successful launch of the Artificial Intelligence
‘agent’, authorities can make informed decisions about safety warnings and susceptibility
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System