How Synthetic Data Can Prevent the Next Big Disaster

The US economy is losing billions of dollars each year due to deteriorating infrastructure issues, such leaking pipelines, fires caused by vegetation encroachment, corroded railways, etc. These scenarios can quickly escalate into disasters, costing human lives and massive financial losses. Protecting against this happening requires careful monitoring of infrastructure. However, the only way to monitor such a large network of infrastructure is by combining geospatial imagery with machine learning algorithms. The problem is that there’s a lack of labeled data to properly train those algorithms. In this presentation I will detail why the solution is to simulate our critical infrastructure and generate synthetic data to train our algorithms to detect the deterioration of infrastructure for preventative maintenance. In addition, I will explain how synthetic data also brings further benefits including:

- Improved algorithms accuracy
- Reduced data collection and annotation time
- Lower costs for data preparation