Paper by Francesca Di Giuseppe, Joe McNorton, Anna Lombardi & Fredrik Wetterhall: “Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting…(More)”.
New AI Collaboratives to take action on wildfires and food insecurity
Google: “…last September we introduced AI Collaboratives, a new funding approach designed to unite public, private and nonprofit organizations, and researchers, to create AI-powered solutions to help people around the world.
Today, we’re sharing more about our first two focus areas for AI Collaboratives: Wildfires and Food Security.
Wildfires are a global crisis, claiming more than 300,000 lives due to smoke exposure annually and causing billions of dollars in economic damage. …Google.org has convened more than 15 organizations, including Earth Fire Alliance and Moore Foundation, to help in this important effort. By coordinating funding and integrating cutting-edge science, emerging technology and on-the-ground applications, we can provide collaborators with the tools they need to identify and track wildfires in near real time; quantify wildfire risk; shift more acreage to beneficial fires; and ultimately reduce the damage caused by catastrophic wildfires.
Nearly one-third of the world’s population faces moderate or severe food insecurity due to extreme weather, conflict and economic shocks. The AI Collaborative: Food Security will strengthen the resilience of global food systems and improve food security for the world’s most vulnerable populations through AI technologies, collaborative research, data-sharing and coordinated action. To date, 10 organizations have joined us in this effort, and we’ll share more updates soon…(More)”.
A US-run system alerts the world to famines. It’s gone dark after Trump slashed foreign aid
Article by Lauren Kent: “A vital, US-run monitoring system focused on spotting food crises before they turn into famines has gone dark after the Trump administration slashed foreign aid.
The Famine Early Warning Systems Network (FEWS NET) monitors drought, crop production, food prices and other indicators in order to forecast food insecurity in more than 30 countries…Now, its work to prevent hunger in Sudan, South Sudan, Somalia, Yemen, Ethiopia, Afghanistan and many other nations has been stopped amid the Trump administration’s effort to dismantle the US Agency for International Development (USAID).
“These are the most acutely food insecure countries around the globe,” said Tanya Boudreau, the former manager of the project.
Amid the aid freeze, FEWS NET has no funding to pay staff in Washington or those working on the ground. The website is down. And its treasure trove of data that underpinned global analysis on food security – used by researchers around the world – has been pulled offline.
FEWS NET is considered the gold-standard in the sector, and it publishes more frequent updates than other global monitoring efforts. Those frequent reports and projections are key, experts say, because food crises evolve over time, meaning early interventions save lives and save money…The team at the University of Colorado Boulder has built a model to forecast water demand in Kenya, which feeds some data into the FEWS NET project but also relies on FEWS NET data provided by other research teams.
The data is layered and complex. And scientists say pulling the data hosted by the US disrupts other research and famine-prevention work conducted by universities and governments across the globe.
“It compromises our models, and our ability to be able to provide accurate forecasts of ground water use,” Denis Muthike, a Kenyan scientist and assistant research professor at UC Boulder, told CNN, adding: “You cannot talk about food security without water security as well.”
“Imagine that that data is available to regions like Africa and has been utilized for years and years – decades – to help inform divisions that mitigate catastrophic impacts from weather and climate events, and you’re taking that away from the region,” Muthike said. He cautioned that it would take many years to build another monitoring service that could reach the same level…(More)”.
AI could supercharge human collective intelligence in everything from disaster relief to medical research
Article by Hao Cui and Taha Yasseri: “Imagine a large city recovering from a devastating hurricane. Roads are flooded, the power is down, and local authorities are overwhelmed. Emergency responders are doing their best, but the chaos is massive.
AI-controlled drones survey the damage from above, while intelligent systems process satellite images and data from sensors on the ground and air to identify which neighbourhoods are most vulnerable.
Meanwhile, AI-equipped robots are deployed to deliver food, water and medical supplies into areas that human responders can’t reach. Emergency teams, guided and coordinated by AI and the insights it produces, are able to prioritise their efforts, sending rescue squads where they’re needed most.
This is no longer the realm of science fiction. In a recent paper published in the journal Patterns, we argue that it’s an emerging and inevitable reality.
Collective intelligence is the shared intelligence of a group or groups of people working together. Different groups of people with diverse skills, such as firefighters and drone operators, for instance, work together to generate better ideas and solutions. AI can enhance this human collective intelligence, and transform how we approach large-scale crises. It’s a form of what’s called hybrid collective intelligence.
Instead of simply relying on human intuition or traditional tools, experts can use AI to process vast amounts of data, identify patterns and make predictions. By enhancing human decision-making, AI systems offer faster and more accurate insights – whether in medical research, disaster response, or environmental protection.
AI can do this, by for example, processing large datasets and uncovering insights that would take much longer for humans to identify. AI can also get involved in physical tasks. In manufacturing, AI-powered robots can automate assembly lines, helping improve efficiency and reduce downtime.
Equally crucial is information exchange, where AI enhances the flow of information, helping human teams coordinate more effectively and make data-driven decisions faster. Finally, AI can act as social catalysts to facilitate more effective collaboration within human teams or even help build hybrid teams of humans and machines working alongside one another…(More)”.
When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
Paper by Sara Marcucci, Stefaan Verhulst and María Esther Cervantes: “The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.
This paper seeks to map out the rapidly evolving domain of anticipatory methods in the realm of migration policy, capturing the trend toward integrating quantitative and qualitative methodologies and harnessing novel tools and data. It introduces a new taxonomy designed to organize these methods into three core categories: Experience-based, Exploration-based, and Expertise-based. This classification aims to guide policymakers in selecting the most suitable methods for specific contexts or questions, thereby enhancing migration policies…(More)”
Combine AI with citizen science to fight poverty
Nature Editorial: “Of the myriad applications of artificial intelligence (AI), its use in humanitarian assistance is underappreciated. In 2020, during the COVID-19 pandemic, Togo’s government used AI tools to identify tens of thousands of households that needed money to buy food, as Nature reports in a News Feature this week. Typically, potential recipients of such payments would be identified when they apply for welfare schemes, or through household surveys of income and expenditure. But such surveys were not possible during the pandemic, and the authorities needed to find alternative means to help those in need. Researchers used machine learning to comb through satellite imagery of low-income areas and combined that knowledge with data from mobile-phone networks to find eligible recipients, who then received a regular payment through their phones. Using AI tools in this way was a game-changer for the country.Can AI help beat poverty? Researchers test ways to aid the poorest people
Now, with the pandemic over, researchers and policymakers are continuing to see how AI methods can be used in poverty alleviation. This needs comprehensive and accurate data on the state of poverty in households. For example, to be able to help individual families, authorities need to know about the quality of their housing, their children’s diets, their education and whether families’ basic health and medical needs are being met. This information is typically obtained from in-person surveys. However, researchers have seen a fall in response rates when collecting these data.
Missing data
Gathering survey-based data can be especially challenging in low- and middle-income countries (LMICs). In-person surveys are costly to do and often miss some of the most vulnerable, such as refugees, people living in informal housing or those who earn a living in the cash economy. Some people are reluctant to participate out of fear that there could be harmful consequences — deportation in the case of undocumented migrants, for instance. But unless their needs are identified, it is difficult to help them.Leveraging the collaborative power of AI and citizen science for sustainable development
Could AI offer a solution? The short answer is, yes, although with caveats. The Togo example shows how AI-informed approaches helped communities by combining knowledge of geographical areas of need with more-individual data from mobile phones. It’s a good example of how AI tools work well with granular, household-level data. Researchers are now homing in on a relatively untapped source for such information: data collected by citizen scientists, also known as community scientists. This idea deserves more attention and more funding.
Thanks to technologies such as smartphones, Wi-Fi and 4G, there has been an explosion of people in cities, towns and villages collecting, storing and analysing their own social and environmental data. In Ghana, for example, volunteer researchers are collecting data on marine litter along the coastline and contributing this knowledge to their country’s official statistics…(More)”.
Advanced Flood Hub features for aid organizations and govern
Announcement by Alex Diaz: “Floods continue to devastate communities worldwide, and many are pursuing advancements in AI-driven flood forecasting, enabling faster, more efficient detection and response. Over the past few years, Google Research has focused on harnessing AI modeling and satellite imagery to dramatically accelerate the reliability of flood forecasting — while working with partners to expand coverage for people in vulnerable communities around the world.
Today, we’re rolling out new advanced features in Flood Hub designed to allow experts to understand flood risk in a given region via inundation history maps, and to understand how a given flood forecast on Flood Hub might propagate throughout a river basin. With the inundation history maps, Flood Hub expert users can view flood risk areas in high resolution over the map regardless of a current flood event. This is useful for cases where our flood forecasting does not include real time inundation maps or for pre-planning of humanitarian work. You can find more explanations about the inundation history maps and more in the Flood Hub Help Center…(More)”.
In the hands of a few: Disaster recovery committee networks
Paper by Timothy Fraser, Daniel P. Aldrich, Andrew Small and Andrew Littlejohn: “When disaster strikes, urban planners often rely on feedback and guidance from committees of officials, residents, and interest groups when crafting reconstruction policy. Focusing on recovery planning committees after Japan’s 2011 earthquake, tsunami, and nuclear disasters, we compile and analyze a dataset on committee membership patterns across 39 committees with 657 members. Using descriptive statistics and social network analysis, we examine 1) how community representation through membership varied among committees, and 2) in what ways did committees share members, interlinking members from certain interests groups. This study finds that community representation varies considerably among committees, negatively related to the prevalence of experts, bureaucrats, and business interests. Committee membership overlap occurred heavily along geographic boundaries, bridged by engineers and government officials. Engineers and government bureaucrats also tend to be connected to more members of the committee network than community representatives, giving them prized positions to disseminate ideas about best practices in recovery. This study underscores the importance of diversity and community representation in disaster recovery planning to facilitate equal participation, information access, and policy implementation across communities…(More)”.
Harnessing AI: How to develop and integrate automated prediction systems for humanitarian anticipatory action
CEPR Report: “Despite unprecedented access to data, resources, and wealth, the world faces an escalating wave of humanitarian crises. Armed conflict, climate-induced disasters, and political instability are displacing millions and devastating communities. Nearly one in every five children are living in or fleeing conflict zones (OCHA, 2024). Often the impacts of conflict and climatic hazards – such as droughts and flood – exacerbate each other, leading to even greater suffering. As crises unfold and escalate, the need for timely and effective humanitarian action becomes paramount.
Sophisticated systems for forecasting and monitoring natural and man-made hazards have emerged as critical tools to help inform and prompt action. The full potential for the use of such automated forecasting systems to inform anticipatory action (AA) is immense but is still to be realised. By providing early warnings and predictive insights, these systems could help organisations allocate resources more efficiently, plan interventions more effectively, and ultimately save lives and prevent or reduce humanitarian impact.
This Policy Insight provides an account of the significant technical, ethical, and organisational difficulties involved in such systems, and the current solutions in place…(More)”.
Humanitarian Mapping with WhatsApp: Introducing ChatMap
Article by Emilio Mariscal: “…After some exploration, I came up with an idea: what if we could export chat conversations and extract the location data along with the associated messages? The solution would involve a straightforward application where users can upload their exported chats and instantly generate a map displaying all shared locations and messages. No business accounts or complex integrations would be required—just a simple, ready-to-use tool from day one.
ChatMap —chatmap.hotosm.org — is a straightforward and simple mapping solution that leverages WhatsApp, an application used by 2.78 billion people worldwide. Its simplicity and accessibility make it an effective tool for communities with limited technical knowledge. And it even works offline! as it relies on the GPS signal for location, sending all data with the phone to gather connectivity.
This solution provides complete independence, as it does not require users to adopt a technology that depends on third-party maintenance. It’s a simple data flow with an equally straightforward script that can be improved by anyone interested on GitHub.
We’re already using it! Recently, as part of a community mapping project to assess the risks in the slopes of Comuna 8 in Medellín, an area vulnerable to repeated flooding, a group of students and local collectives collaborated with the Humanitarian OpenStreetMap (HOT) to map areas affected by landslides and other disaster impacts. This initiative facilitated the identification and characterization of settlements, supporting humanitarian aid efforts.
Photo by Daniela Arbeláez Suárez (source: WhatsApp)
As shown in the picture, the community explored the area on foot, using their phones to take photos and notes, and shared them along with the location. It was incredibly simple!
The data gathered during this activity was transformed 20 minutes later (once getting access to a WIFI network) into a map, which was then uploaded to our online platform powered by uMap (umap.hotosm.org)…(More)”.
See more at https://umap.hotosm.org/en/map/unaula-mapea-con-whatsapp_38