Every year, landslides kill more than 4,000 people and cause an estimated US$20 billion in economic losses worldwide. Homes are buried, roads disappear, and entire communities are cut off in a matter of minutes. Yet landslides remain one of the most overlooked natural hazards in global disaster planning.
A new World Bank research paper by Priscilla Niyokwiringirwa, Tjark Gall, and Abhas K. Jha argues that the real problem is not a lack of technology. Scientists at institutions such as NASA, the International Association of Engineering Geology and the Environment, and the United States Geological Survey have developed advanced tools to predict slope failures. What is missing is something more basic and more powerful: reliable, standardized data on where and when landslides occur.
Without accurate records, even the best models cannot produce trustworthy forecasts.
Why the Risk Is Growing
Landslides hit hardest in developing countries, especially in mountainous and tropical regions across Asia, Africa, and Latin America. Rapid population growth and urban expansion are pushing people onto steep, unstable slopes. Roads cut into hillsides weaken natural terrain. Forests are cleared, removing protective root systems.
Climate change is making matters worse. Extreme rainfall is becoming more intense in many regions. Glaciers are melting faster. Permafrost is thawing. All of these changes increase the likelihood that slopes will collapse.
When disasters strike, the impacts go far beyond immediate loss of life. The 2017 mudslide in Freetown, Sierra Leone, killed more than 1,000 people. In 2015, the Gorkha earthquake in Nepal triggered over 10,000 landslides. Roads, bridges, hydropower plants, and schools were destroyed. Years of development progress were wiped out in days.
Despite this, landslides are often treated as secondary disasters. Smaller events in remote areas may never be officially recorded. This makes the overall risk appear smaller than it truly is.
The Data Problem No One Talks About
Modern disaster management relies heavily on prediction. Today’s models use machine learning tools such as random forests and neural networks to identify areas at risk. These tools can analyze rainfall, slope angle, soil type, and land cover to predict where landslides are likely to happen.
But these systems depend on records. If the data is incomplete or inaccurate, predictions will also be flawed.
Many global landslide databases are based mainly on media reports. This means they often capture only large events near populated areas. Small landslides or those in remote regions may be missed entirely. As a result, models may end up learning patterns of human settlement rather than true geological instability.
A case study in Nepal’s Hindu Kush Himalaya region shows the challenge. Researchers were able to build a landslide susceptibility model using available data, and the results looked statistically strong. However, they found that many important details were missing, such as exact dates, volumes, and soil conditions. The data was simply not rich enough to support more advanced forecasting or early warning systems.
A Three-Step Plan for Better Data
To fix this problem, the researchers propose a simple three-tier system for improving landslide data.
The first tier is basic. Countries record where a landslide happened, when it occurred, and a short description. This helps identify high-risk areas and build awareness.
The second tier adds more detail. Records include precise coordinates, exact dates, landslide type, trigger such as rainfall or earthquake, and size information. With this level of data, governments can build national risk maps and develop rainfall thresholds for early warning systems.
The third tier is the gold standard. It includes detailed mapping of landslide areas, soil properties, runout distances, exposure of buildings and infrastructure, and even real-time monitoring using satellites and sensors. This level allows for advanced modeling and highly targeted warnings.
The system is designed to be flexible. Countries can start small and improve over time.
Data as Lifesaving Infrastructure
The core message of the report is clear: landslide data should be treated as essential infrastructure. Just as roads and bridges need investment, so do national data systems.
As climate change increases extreme weather and development expands into hazard-prone areas, the cost of poor information will grow. Without reliable inventories, governments cannot issue timely warnings, design safer roads, or protect vulnerable communities.