Delivering food through conflict zones, minefields, and floods can put humanitarian workers at mortal risk. Now, technology that’s been developed for
Delivering food through conflict zones, minefields, and floods can put humanitarian workers at mortal risk. Now, technology that’s been developed for controlling rovers on distant planets is being adapted to take aid workers out of some of the world’s most dangerous missions. Project AHEAD, a collaboration between the World Food Programme, Germany’s aerospace research centre DLR, the Red Cross, and various tech partners, is working on remotely operated vehicles. These vehicles are capable of carrying supplies through areas deemed too perilous for conventional delivery trucks.
Footage from a DLR test site in Germany shows a SHERP all-terrain vehicle wading into open water and navigating rough terrain. With sensors scanning the landscape ahead, the operator can control the vehicle remotely, allowing it to travel without anyone behind the wheel. This innovative system draws on DLR’s expertise in developing remotely operated and autonomous planetary rovers, including the MMX rover designed to explore Phobos, one of Mars’s moons. But the use of this emerging tech in humanitarian efforts goes beyond just physical deliveries.
Check this out, Hunger Live! It’s a publicly available platform created by the World Food Programme that utilizes machine learning and near-real-time data to track food insecurity across over 95 countries. It combines information on factors like conflict, weather, climate hazards, and economic conditions to help identify emerging hunger crises. “Everybody can check it out, Hunger Live, on the internet. You can get real-time data, and right now, we’re even looking into forecasting food security 90 days into the future,” said Bernhard Kowatsch, director of the WFP’s Global Accelerator and Ventures division.
Reliable data is also crucial for humanitarian responses. Without adequate information on roads, buildings, and population centers, aid workers may find it tough to decide where to evacuate people, set up shelters, or deliver supplies. After two powerful earthquakes struck northern Venezuela back in June, limited geographical data made it hard to assess the damage and prioritize assistance. The Humanitarian OpenStreet Team used machine learning to extract building information from satellite imagery. Volunteers then reviewed the images through its Swipe app, marking areas where structures seemed damaged.
“Within four days after the earthquake, we mobilized more than 600 volunteers who were swiping left and right on the mobile app, indicating: yes, this building area is damaged, no, this building area is not damaged,” explained Leen D’hondt, director of technology and data at the Humanitarian OpenStreet Team. “And that actually helped early responders go to the right areas for food delivery and for all the other necessities we might need right after the earthquake,” D’hondt added.
For all the speed that AI can add, D’hondt mentioned that the technology still can’t match the accuracy of detailed work done by human beings. “Manual mapping still provides the best quality. However, sometimes speed is more important,” she said. “Sometimes it’s more important to know roughly where the buildings are. They’re not perfectly mapped, but we know how many people live in that area. That’s where AI and machine-learning models come into play right now.”
Despite rapid advancements, insiders say that such systems are still far from being routinely integrated into emergency responses globally. “Currently, there aren’t really systems incorporated into these emergency protocols in most countries,” stated Monique Kuglitsch, innovation manager at the Fraunhofer Heinrich Hertz Institute. “There are exceptions. In India, they have an operational AI-based early-warning system. Also in Europe, we have an AI forecasting system from the European Centre for Medium-Range Weather Forecasts, which is operational. But in many countries, it’s still experimental.”
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Kaynak: Orijinal Haber