“Roofs are a proxy of judging how vulnerable a building is. The SEEDS team quickly zeroed in on roof types. The team had to find some attributes that could act as a proxy for the AI model to identify homes that would be vulnerable. To build the AI model, they had to know who those people would be, and how they’d be impacted. SEEDS wanted to use AI to generate house-level advisories for communities at risk, so it could warn those who had the most to lose. While state and local governments issue advisories in the event of an impending cyclone, they contain mostly generic information and cover entire districts. For days, we depended on the goodwill of others for food and survival,” she says. “We lost all our wares to the storm and had no other means of livelihood. Her house was destroyed, but it was the loss of her food stall that hit her the hardest. In November 2020, she lost all her belongings to Cyclone Nivar. Sometimes, however, good fortune is not enough. So far, she’s depended on good fortune and an overabundance of caution to protect her family from the tropical storms. The sea, while a source of income for her, is also a source of constant worry. She shares her home-a hut made of bamboo and mud with palm leaves substituting for a roof-with her three daughters. But the sea has now moved closer to our homes,” she says in her native Telugu language. “When I was a child, I had to walk for nearly a mile to reach the beach. Satyavati doesn’t need any report to convince her about the impact of climate change on her community. It found that extreme weather conditions like droughts, hurricanes, and heatwaves have intensified because of climate change. A report by the Intergovernmental Panel on Climate Change (IPCC), released in August this year, declared a “code red” for humanity. This enables SEEDS and its on-ground partners to pinpoint those who’d be at the highest risk from the cyclone and focus their outreach to those communities.Īccording to the United Nations, the global mean sea level has risen faster since 1900, than over any preceding century in at least the last 3,000 years. It then applies advanced data analytics and machine learning to identify the most vulnerable houses. The AI model, called Sunny Lives, utilizes high-resolution satellite imagery of areas likely to fall under the cyclone’s path. It is a global grant program designed to help find solutions to challenges centered around disaster response, refugees, displaced people, human rights, and the needs of women and children. Microsoft launched AI for Humanitarian Action in 2018.

It was founded in 1994 and works with vulnerable communities to provide relief and rehabilitation after disasters. SEEDS stands for the Sustainable Environment and Ecological Development Society. “We started conversations with Microsoft to see if we could use AI to identify these houses so we could spend more time on the ground helping people.” Manually collecting information about houses that’d be vulnerable is a very time-intensive task and we can, at best, evaluate 100 houses in a day,” says Raghav Ranganathan, technical director, SEEDS. “We usually come to know a cyclone’s predicted path 48 hours or so before it makes landfall. She even managed to move the contents of her food stall to a neighbor’s concrete house that could withstand the high-velocity winds. The advisory, which she and nearly a thousand other families received, was the result of an artificial intelligence (AI) model by SEEDS, a non-profit that had recently received a Microsoft AI for Humanitarian Action grant.įollowing the instructions on the sheet of paper, her family packed their belongings and left for a nearby cyclone shelter, one of the hundreds created after the 1999 super-cyclone. Satyavati received a personalized advisory printed in her language a day before the cyclone made landfall. Cyclones–known elsewhere as hurricanes and typhoons–are intense tropical storms that originate in the Indian Ocean.īut in May this year, when Cyclone Yaas was about to make landfall, things were a little different. Winds of up to 160 miles an hour (257 kilometers an hour) killed thousands and left behind a trail of destruction. She was among the lucky ones who survived a “super cyclone” that tore through the state of Odisha on India’s east coast in 1999.

We prayed for the storm to pass, but we lost everything we had,” says Kalpreddy Satyavati, who runs a tiny food stall catering to the local fishing community in the coastal town of Puri. “We fled to the nearby forest and clung on to swaying trees for our lives.

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