Case Study: a leading global security and aerospace company combats wildfires with Appen

Combating Wildfires with Computer Vision Saving the Planet with AI, One Forest at a Time

A leading global security and aerospace company needed to train high-quality computer vision models to predict wildfire behavior for its decision support systems. The challenge was acquiring accurate data labeling and model evaluation at scale using complex electro-optical, infrared, and heat imagery. Appen's data annotation platform was used to ensure the predictive models for tracking fire spread were reliable.

Appen provided a computer vision automation solution that supported multi-sensor integration and a variety of label types, such as polygons and pixel masks, to extract critical features from imagery. A key benefit was the ability to annotate data at its initial storage point, saving significant time and mitigating risk by allowing the customer to retain full data custody. The platform enabled the customer's data scientists to utilize new data sources, improve model performance, and have high confidence in the accuracy of their training data.