Thunderstorms and large amounts of precipitation present a major commercial risk to farmers. To prevent harvest damages due to severe weather events, farmers conclude an insurance and pay an insurance premium to be compensated in case of damages. To provide a timely and competent service to farmers, insurance companies need to be able to process claims from severe weather events in a short period of time.
Today, claim processing is mostly a manual process involving on-site visits by the insurance company. This is necessary due to missing information on the damage by field or farmer. To validate the farmer's compensation request, insurance representatives visit the affected fields to propose a compensation amount to the farmer. As many farmers might request compensation payments at the same time, this is a lengthy and stressful process for both sides, resulting in a suboptimal client experience and high cost for the insurance company.
To establish a geospatial record of the covered fields, the farmer needs to indicate the fields that shall be included in the insurance. The field boundaries and corresponding crop types are saved in the insurance companies' database as a shape or GeoTIFF file. Monitoring the growth status of the crop throughout the year can be automated with optical satellite data (e.g. Sentinel-2) as input data and a deep learning model, e.g. convolutional neural networks (CNN), can be developed.
In case of a severe thunderstorm, satellite images from a couple of days after the event can be analysed automatically, measuring the area and the percentage of crops damaged. By analyzing past compensation payments and their corresponding satellite imagery ('ground truth data'), a prediction for immediate payout can be provided to the farmer. The immediate payout can later be adjusted when a more detailed analysis by inspection of satellite imagery or on-site inspections is conducted.
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