Validated data-driven decisions to save lives

The Solution of The Future Firefighters
for the challenge of the Dutch Public Safety Institute

WINNING TEAM:
THE FUTURE FIREFIGHTERS

THE CHALLENGE

How to validate and distribute
validated data in a crisis situation?

In a real crisis or catastrophe, we want both fully-informed rescuers on the scene and a public fully aware of how to be safe and/or help. In this chaotic context, everyone should be able to rely on validated data. Every crisis has a related data field. The richer and more connected the data field, the better. This data field consists of both open (the general public, internet of things, databases) and closed (professional rescue organisations and ‘crisis partners’) data.

Nowadays, citizens can collect a lot of valuable information through their smartphones or other connected devices. This means they can potentially help both emergency services and (potential) victims during incidents and catastrophes.

To obtain a comprehensive, validated picture of the situation, the Emergency and Crisis Management Centre (ECMC) needed a solution that would combine open data and closed data and enable people to help validate the situation. For example, the ECCC could ask civilians present on the scene if a fire is still burning, or to take pictures of the situation.

SOLUTION

#odysseyhack

Validated data-driven decisions to save lives

Emergency responders can have either too little or too much (unfiltered and unvalidated) data in dealing with a disaster. This paradox should be tackled to reduce the possible disastrous consequences of making uninformed decisions under stress.

As the different data sources from which we can get information during an emergency situation are huge, unstructured and most of the times unusable in real-time. How to benefit from all this data and get the most out of it to save lives?

The teams’ solution is an Artificial Intelligence Disaster Advisor (AIDA), which enables rescuers to make faster and better decisions.

The solution integrates all sources of information (e.g. static data such as GIS, Emergency & Risk Plans, 2D & 3D Building Blueprints, Standard Operational Guidelines, Historic or Open Data and real-time data such as cameras, radio, GPS and IoT), processes and simplifies this information. It delivers actionable advice to commanders on what they could do.

Data is passed through the system, which uses machine learning models to filter out the relevant data, distill insights from it and make predictions and suggestions. The routing mechanism makes sure that the right data reaches the right people as soon as possible. Furthermore, the system learns over time and improves itself constantly.

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