Artificial intelligence is often framed as a threat to humanity, but across Europe, humanitarian organisations are turning it into a lifeline. From remotely operated vehicles inspired by Mars rovers to machine-learning platforms that predict hunger crises, the continent's tech sector is quietly reshaping how aid reaches the world's most dangerous places.
One of the most striking examples is Project AHEAD, a collaboration between the World Food Programme (WFP), Germany's DLR aerospace research centre, the Red Cross, and several technology partners. The project adapts technology originally developed for planetary rovers—including the MMX rover built to explore Phobos, a moon of Mars—to create remotely operated vehicles capable of carrying supplies through minefields, floodwaters, and conflict zones.
Footage from a DLR test site in Germany shows a SHERP all-terrain vehicle wading into open water and climbing over rough ground. Sensors scan the terrain ahead while an operator controls the vehicle remotely, allowing it to travel without anyone sitting behind the wheel. The goal is to take human aid workers out of some of the most perilous missions on Earth.
Predicting hunger with machine learning
Beyond physical deliveries, AI is also transforming how aid organisations anticipate crises. HungerMap Live, a publicly available platform developed by the WFP, uses machine learning and near-real-time data to track food insecurity across more than 95 countries. It combines information on conflict, weather, climate hazards, and economic conditions to help identify emerging hunger crises.
“Everybody can check it out, HungerMap 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.
This kind of predictive capability is especially critical in a continent where climate change and geopolitical instability are increasingly intertwined. The European Centre for Medium-Range Weather Forecasts, for instance, already operates an AI-based forecasting system that is used operationally across Europe, though similar systems remain experimental in many other parts of the world.
Mapping disasters at speed
Reliable maps are another cornerstone of effective humanitarian response. Without accurate information about roads, buildings, and population centres, aid workers struggle to decide where to evacuate people, establish shelters, or deliver supplies. After two powerful earthquakes struck northern Venezuela in June, limited geographical data made it difficult to assess the damage and prioritise assistance.
The Humanitarian OpenStreetMap Team (HOT) stepped in, using machine learning to extract information about buildings from satellite imagery. Volunteers then reviewed the images through its MapSwipe app, marking areas where structures appeared damaged.
“Within four days after the earthquake, we were able to mobilise more than 600 volunteers that were basically swiping left and right on the mobile app, indicating: yes, this building area is damaged; no, this building area is not damaged,” said Leen D’hondt, director of technology and data at HOT. “And that actually helped early responders to go to the right areas for food delivery and for all the other necessities that we might need right after the earthquake.”
For all the speed AI can add, D’hondt cautioned that the technology cannot yet match the accuracy of detailed work carried out by human mappers. “Manual mapping still provides the best quality. However, sometimes speed is more important,” she said. “Sometimes it’s more important to know more or less where the buildings are. They’re not perfectly mapped, but we know how many people are living in that area. And that’s where AI and machine-learning models come into the picture right now.”
Despite rapid advances, insiders say such systems are still far from being routinely incorporated into emergency responses around the world. “Right now, there aren’t really systems integrated into these emergency protocols in most countries,” said Monique Kuglitsch, innovation manager at the Fraunhofer Heinrich Hertz Institute. “There are exceptions. In India, they do have an AI-based early-warning system that is operational. Also in Europe, we have an AI forecasting system from the European Centre for Medium-Range Weather Forecasts, which is operational. But in a lot of countries, it’s still experimental.”
The push to integrate AI into humanitarian work is part of a broader trend across Europe, where institutions like the DLR and Fraunhofer are increasingly collaborating with international organisations. As the technology matures, the hope is that these tools will become standard in emergency response—not just in Europe, but globally.
