How AI is Improving Disaster Preparedness

Disaster preparedness saves lives and reduces the cost of recovery. The United Nations Office for Disaster Risk Reduction estimates that every US$1 invested in risk reduction and prevention can save up to US$15 in post-disaster recovery, whilst every US$1 invested in making infrastructure disaster-resilient saves US$4 in reconstruction.

Preparedness can take the form of predicting and mitigating potential disasters, or making sure that existing defences are hardened against potential risks. This crucial task is now being supported by advances in machine learning and AI. A wide range of computer vision powered systems are in development that promise to enhance disaster preparedness efforts across the globe.

The success of these efforts is in part reliant on the adequate provision of precisely annotated training data for machine learning. This blog will focus on three vital applications for AI technology in this sector and show how outsourcing to annotation professionals, like Keymakr, can streamline future development.

Analysing risks to buildings

Different structures carry different associated risks when confronted with natural disasters. In zones of seismic activity larger office buildings may be able to withstand earthquakes that smaller dwellings cannot. Surveying at risk areas of habitation allows disaster preparedness planners to focus strengthening efforts on the buildings that are most vulnerable.

This time consuming process can be greatly simplified with the help of deep learning techniques. Annotated images of buildings and street views can train models to locate structure types that are in need of reinforcement. This technology can make cities more resilient to earthquakes and storms by providing authorities with granular information.

For developers trying to refine this disaster preparedness technology it is vital that training images are annotated with a high degree of accuracy. Crowdsourcing this annotation work can often result in errors. Dedicated annotation services, with managed teams of experienced operators, are guaranteed to produce more precise annotations and more effective datasets in general.

Monitoring and managing fire risks

Forest fires cause billions of dollars of damage and disrupt countless lives every year. It is often hard to predict when and where these fires will occur across large forested areas, meaning that many fires are only identified when they are already of a considerable scale.

Careful monitoring of land use, moisture conditions, and vegetation cover can allow for early intervention in the most at risk areas. Computer vision based AI models can integrate this data and use it to analyse satellite imagery of forested regions, providing automated risk assessments that can guide preparations.

The raw material for fire monitoring technology is annotated satellite data, and it is essential that these annotations reflect real world conditions accurately. Professional annotation services can often be best placed when it comes to ensuring robust quality control in data sets. Keymakr, for example, employs three levels of human verification alongside automated checks.

Real-time disaster alerts

Natural disasters can occur suddenly and without warning. Earthquakes and tornados can quickly devastate communities, leaving emergency services struggling to mount an effective response. This delay in action could be helped by AI monitored CCTV cameras.

These cameras, powered by machine learning, should have the capacity to recognize natural disaster events as they occur, and send real-time alerts to the relevant authorities. This could be vital when a flood or seismic event strikes an isolated community, as emergency alerts can be sent to first responders across a wide area.

To fully realise the potential of these monitoring technologies it is vital that innovators have access to annotated video data. Proprietary tools, offered by annotation providers, can make image and video labeling much more efficient, ensuring a constant flow of training data as models grow and develop.

Experienced annotation providers

Keymakr’s teams of experienced annotators, supported by rigorous management processes, are able to produce bespoke datasets to meet any need.

Contact a team member to book your personalized demo today.