This non-profit is defending weak communities from the consequences of local weather change with AI


AI to the rescue

“We didn’t have another instance of AI being used to tag roof types to forecast damage due to hurricanes. In addition, there was no readily available training data,” says Tina Sederholm, a senior program supervisor within the AI for Good Research Lab at Microsoft, who led the challenge with information scientists.

“From a technical standpoint too, it was difficult because there is no urban planning in areas that we were targeting, and the population was so dense that it was difficult to first differentiate individual houses and categorize them accurately based on their roof type. But we built a machine learning model to counter these problems,” explains Md Nasir, a knowledge scientist and researcher within the AI for Good Research Lab.

To create the much-needed coaching information, Gramener, with its experience in geospatial options, stepped in to ship a scalable answer. Its information scientists accessed excessive decision satellite tv for pc imagery and manually tagged greater than 50,000 homes to categorise their roofs underneath seven classes relying on the fabric used to assemble them.

“We wanted to identify the building footprint and distinguish between two houses distinctly. But informal settlements do not often have well defined boundaries and they are generally the worst impacted in any disaster,” says Sumedh Ghatage, a knowledge scientist from Gramener, who labored on constructing the AI mannequin. “Secondly, as the geographical location changes, the types of roofs change as well. But we wanted to identify all kinds of roofs, to ensure the final model could be deployed in any region.”

This shaped the idea of the coaching information Nasir required. After making an attempt a couple of completely different strategies, his last mannequin might establish roofs with an accuracy of practically 90%. But that was only the start.

an image that shows how the AI model identified roof types
After making an attempt a couple of completely different strategies, the ultimate AI mannequin might establish roof varieties from satellite tv for pc imagery with an accuracy of practically 90%

“Apart from roofs, we considered nearly a dozen critical parameters that determine the overall impact cyclones would have on a house,” says Kaustubh Jagtap from Gramener, who led the information consulting bits for the challenge. “For example, if a house is closer to a water body, it would be more likely to be impacted due to a cyclone-induced flood. Or if the area around the house is covered by concrete, the water won’t percolate into the soil below and odds of water logging and flooding would be higher.”

The staff at Gramener then added different layers to the mannequin. The alignment of all of the completely different layers together with street networks, proximity to water our bodies, elevation profiles, vegetation, amongst others was a tedious process. Gramener created an Azure machine studying pipeline, which robotically captures the information and produces threat rating profiles for each home.

It took about 4 months for the Sunny Lives mannequin to turn out to be a actuality and it was piloted throughout cyclones that hit southern Indian states of Tamil Nadu and Kerala in 2020. But it was throughout Cyclone Yaas in May this yr that it was deployed at scale.

As quickly as the trail of Cyclone Yaas was predicted, the staff at Gramener procured excessive decision satellite tv for pc imagery of densely populated areas that’d be impacted and ran the Sunny Lives AI mannequin. In a couple of hours, they have been in a position to create a threat rating for each home within the space.

A satellite image of Puri with the risk profile from Cyclone Yaas for individual houses generated by Sunny Lives AI model.
A satellite tv for pc picture of Puri with the chance profile from Cyclone Yaas for particular person homes generated by Sunny Lives AI mannequin.

Gramener additionally assisted in sampling strategies and validated the accuracy of the mannequin with precise floor fact data.

“Earlier, we used to deploy volunteers who manually conducted surveys. Now, all we need to do is procure high-resolution satellite imagery, run the model to determine an area’s vulnerability and get the risk score results within a day. This kind of capacity was unthinkable earlier,” says Garg.

Once the homes have been recognized, SEEDS together with its on-ground companions fanned out into the communities and distributed advisories to just about 1,000 households in native languages like Telugu and Odia, which is spoken by the residents. Each advisory had detailed directions on how they may safe their houses and the place they would want to relocate to earlier than the cyclone made landfall.

The mannequin has opened a world of potentialities. SEEDS believes it may be deployed in lots of nations in Southeast Asia that share comparable dwellings and communities that face the intense ranges of storm threat.

It can be used to manage different climate challenges. For occasion, SEEDS is taking a look at utilizing the mannequin to establish houses in densely populated city areas that is likely to be prone to heatwaves as temperatures hit new information each summer season.

“During a heatwave, roofing becomes the most important parameter because maximum amount of the heat gained in the house happens through the roof. Houses with tin sheets often have poor ventilation and are the most vulnerable at this time,” explains Garg.

There are different tasks being piloted too. For occasion, they’re wanting if AI could possibly be used to establish weak homes within the Himalayan state of Uttarakhand, which is vulnerable to earthquakes.

“We brought our disaster expertise to the table, but Microsoft’s data science made it possible for us to develop the model from scratch,” says Ranganathan.

“The Sunny Lives AI model that the SEEDS and Gramener teams have created is a leading-edge humanitarian solution that is already saving lives and helping to preserve the livelihoods of people most at risk of natural disasters,” says Kate Behncken, vp and lead of Microsoft Philanthropies. “The ingenuity and collaboration between these teams is impressive, and I am encouraged by the promise that this solution holds to help better protect people for other severe weather scenarios, such as heat waves. This is exactly the kind of impact we’re looking to support and drive with NGO partners via the AI for Humanitarian Action program.”

Inspired by the outcomes, SEEDS has began constructing its personal technical capabilities after receiving the AI for Humanitarian Action grant from Microsoft.

“At the end of first year, we also started getting consultants to maintain and improve the accuracy of the model. Microsoft has given us access to the source code, so we may reach a stage soon where we will be able to run the model ourselves,” provides Ranganathan.

Synesy.org