Extreme weather is a significant hurdle both for commercial and individual consumers. Predicting weather has been a great help in minimizing the risks involved. Global weather conditions are getting dynamic with every passing day. The ultimate result is severe weather conditions from storms, floods, fires, and extreme temperatures. Seasonal weather forecasting can be a major source of minimizing extreme weather’s human and socioeconomic costs. Currently, AI is applied for weather forecasting using the data variables like temperature, humidity, pressure, and wind speed. In this article, we aim to look deeper into the data annotation services that can be applied with AI technologies to predict extreme weather better.
Predicting Extreme Weather with AI- a dream come true
The AI technology applied for weather prediction was initiated when Meteorologists and scientists at Penn State University joined hands to investigate more than 50,000 weather satellite images. The purpose was to apply AI and data annotation services to forecast storms. The team in this research studied cloud formations as the basic signs of extreme weather, such as thunderstorms, hail, high winds, and blizzards. Eventually, AI could identify a specific intensity of clouds that would result in cloud formation.
The satellite data gathered for weather forecasts contains multiple data sets apart from weather patterns. Information such as soil moisture, cloud patterns, and drought stress are also gathered. The AI system handles such a complex treasure of data. It is because of AI that local and international forecast is helping millions of users all around the world.
Although automated systems use powerful and large-scale supercomputers to process big data inaccurate weather predictions, the AI system is versatile enough. It has enabled users to run this technology on a smaller scale. Computers make the technology available to a larger market. It can now cut down on high costs as these techniques can be applied on a downscale at the local level. The machine learning technique involves two neural networks working against each other. Neural networks are just like human brain neurons based on global weather.
AI and data annotation- changing daily life with weather prediction
In the initial studies of weather forecasts, satellite images were used to train the AI system applied. An image annotation service was then applied to label the specific cloud formations related to severe weather conditions. Various types of data annotation, specifically image annotation involved. The specific type of data annotation depends on the level of detail required. The data annotation services can label the cloud formations, or these services can contour them with polygon annotation. For specific systems, semantic segmentation is applied. So, data annotation serves as the hallmark of the weather forecast.
Manual data annotation services for weather forecast
Manually annotated data is more reliable and of higher quality as compared to data annotated by large machines. Specialized services like semantic segmentation, polygons, labeling, and point and landmark annotations are applied for weather forecasting. These image annotation services, along with AI, are the source of success for multiple organizations all around the globe.
