Weather Recognition

Weather recognition research focuses on accurately identifying and classifying weather conditions from various data sources, including images and sensor readings, aiming to improve the reliability and performance of systems operating in diverse weather environments. Current approaches leverage machine learning, employing algorithms like Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), often integrated with Transformers or Mixture-of-Experts (MoE) models for enhanced accuracy and handling of complex, multi-label scenarios. This field is crucial for applications ranging from autonomous vehicle navigation and traffic management to improved weather forecasting and hazard prediction, driving advancements in both computer vision and environmental monitoring.