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Fusing AI into satellite image analysis to inform rapid response to floods • UN Global Pulse

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When a natural disaster hits, rapid responses are essential to assess the damage quickly and to direct humanitarian relief efforts where they are needed the most. Experts and data scientists from UNITAR-UNOSAT and UN Global Pulse applied Artificial Intelligence (AI) to satellite imagery to quickly map flooded areas and assess the damage that was caused in various operational settings. In this blog, we describe the approach and how we’re using it to inform decision-making on the ground.

Satellite image analysis for disaster mapping

Flooding is the most frequent type of disaster, affecting more than 2 billion people in the 20 years between 1998 and 2017 alone. When disaster hits, it is of vital importance to quickly know which areas have been affected in order to assess the potential damage and inform humanitarian relief efforts.

Within the United Nations Institute for Training and Research (UNITAR), the Operational Satellite Applications Programme (UNOSAT) supports teams on the ground to assess the impact of floods through its rapid mapping service. How this works is by having experienced analysts produce maps, reports, and data from satellite images to assess the extent of the damage.

On average, UNOSAT responds to 17 flood events per year. Maps of flooded regions are often turned around and delivered to relief efforts to support operations within 2 days. However, with the number of incidents on the rise, UNOSAT has been looking for ways to leverage technology, in particular AI, to automate some of these processes to produce ongoing results and free up time for analysts to perform other tasks.

Here is where the collaboration with UN Global Pulse, the UN Secretary-General’s digital innovation, real-time data and AI initiative was born. Our teams have been closely working for the past several years to inject new technology into satellite image analysis. Flood mapping is one of the areas where our collaboration has unearthed fruitful results.

Experimenting with AI methodologies for rapid flood mapping

The current methods used by UNOSAT’s rapid mapping team are semi-automated, which means that even though some parts of the process are performed automatically, there remain many tasks that a geospatial analyst has to manually carry out. This limits the speed with which a map can be produced, and also the number of maps an individual analyst can produce at a time.

Computing and machine learning at scale now offer efficient automatic methods, which can improve the timeliness of response to a disaster. The creation of an end-to-end pipeline, whereby images of flood-prone areas are automatically downloaded and processed by machine learning algorithms to output disaster maps, could shorten the time needed for analysts to produce and deliver these maps. This would in turn increase the impact to end beneficiaries during humanitarian crises by allowing for the implementation of live streaming mapping services triggered by direct partner requests or automatic activations.

We therefore asked ourselves: How can we inject AI technology to speed up the process, while maintaining the high quality of maps?

Using UNOSAT’s extensive archive of historic flood maps, an AI algorithm was first trained to identify flooded areas. The training data consisted of satellite images collected from the ESA Copernicus Sentinel-1 satellites, and the corresponding human-created flood maps. The dataset spanned 15 historic flood events from across nine countries. Once the algorithm was trained, it was tested in regions the model had never before been trained on. This was done to see how accurate the model could be in identifying flooded regions in a new environment. The AI model surpassed expectations by achieving accuracy scores of above 97%.

However, just because the model performed well in theory, doesn’t mean it would consistently perform the same in real life scenarios, particularly as floods can occur all over the world and using the model in geographies which ‘look’ very different to those where it was trained may confuse it.