After natural disasters, an insurance company wants to quickly and cost-effectively assess the damage to its insured buildings. This requires an infrastructure that can process a large amount of image material in a timely manner. In addition, a model must be developed that allows the prediction for different building types and regions.
Various machine learning models are developed, evaluated and made available. These are the basis for automatic damage detection. A pipeline is built in the Amazon AWS Cloud that seamlessly integrates both the model training and the productive use of the results. Due to the complete integration of all processes in the AWS infrastructure, the product is scalable and can be automatically adapted to the current demand in a cost-efficient manner during idle and load times.
The models can be trained and adapted in a scalable pipeline. Via an API, the results can be used for a wide range of applications for automatic damage detection. Damage classification makes it easier to estimate payout amounts and initiate payout processes automatically.
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