Visual Weather Analytics-Leveraging Image Recognition for Weather Prediction

Image classification is a procedure that makes an effort to understand an image in its entirety. The objective is to label the picture so that it may be filed away in the appropriate category. Image data that has been collected typically suffers from degradation due to opaque means such as haze, smoke, fog, rain, or snow. De-weathering is the procedure that removes the effects of weathering from an image so that it can be used again. This is done so that similar issues do not arise. As a result, the preprocessing phase is required to concurrently evaluate the quality and other characteristics of an input image, and then additionally execute de-weathering (for example, removals of haze, rain, or snow). These checks and removals must take place in sequence. In this research, an idea for a deep learning-based weather image recognition model is presented. The model takes into account 11 various weather situations, including dew, fog/smog, frost, glazing, hail, lightning, rain, rainbow, rime, snow, and sandstorm. In addition, the paper gives an idea for a deep learning-based weather image recognition model.