Consider the days after a hurricane strikes. Trees and debris are blocking roads, bridges are destroyed, and sections of roadway are washed out. Emergency managers soon face a bevy of questions: How can supplies get delivered to certain areas? What's the best route for evacuating survivors? Which roads are too damaged to remain open? Without concrete data on the state of the road network, emergency managers often have to base their answers on incomplete information. The Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory hopes to use its airborne lidar platform, paired with artificial intelligence (AI) algorithms, to fill this information gap.
Consider the days after a hurricane strikes. Trees and debris are blocking roads, bridges are destroyed, and sections of roadway are washed out. Emergency managers soon face a bevy of questions: How can supplies get delivered to certain areas? What's the best route for evacuating survivors? Which roads are too damaged to remain open?
Without concrete data on the state of the road network, emergency managers often have to base their answers on incomplete information. The Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory hopes to use its airborne lidar platform, paired with artificial intelligence (AI) algorithms, to fill this information gap.
To provide the status of the road network, the lidar map is first run through a neural network. This neural network is trained to find and extract the roads, and to determine their widths. Then, AI algorithms search these roads and flag anomalies that indicate the roads are impassable. For example, a cluster of lidar points extending up and across a road is likely a downed tree. A sudden drop in the elevation is likely a hole or washed out area in a road.
The extracted road network, with its flagged anomalies, is then merged with an
OpenStreetMap of the area (an open-access map similar to Google Maps). Emergency
managers can use this system to plan routes, or in other cases to identify
isolated communities — those that are cut off from the road network. The system
will show them the most efficient route between two specified locations, finding
detours around impassable roads. Users can also specify how important it is to
stay on the road; on the basis of that input, the system provides routes through
parking lots or fields.
This process, from extracting roads to finding damage to planning routes, can be
applied to the data at the scale of a single neighborhood or across an entire city.
How fast and how accurate?
To gain an idea of how fast this system works, consider that in a recent test, the team flew the lidar platform, processed the data, and got AI-based analytics in 36 hours. That sortie covered an area of 250 square miles, an area about the size of Chicago, Illinois.
But accuracy is equally as important as speed. "As we incorporate AI techniques into decision support, we're developing metrics to characterize an algorithm's performance," Council says.
For finding roads, the algorithm determines if a point in the lidar point cloud is "road" or "not road." The team ran a performance evaluation of the algorithm against 50,000 square meters of suburban data, and the resulting ROC curve indicated that the current algorithm provided an 87 percent true positive rate (that is, correctly labeled a point as "road"), with a 20 percent false positive rate (that is, labeling a point as "road" that may not be road). The false positives are typically areas that geometrically look like a road but aren't.
The team is continuing to test, train, and tweak their algorithms to improve accuracy. Their hope is that these techniques may soon be deployed to help answer important questions during disaster recovery.
Sources:
MIT News
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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