Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide.

中文翻译：

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K近邻分类在静态网络摄像头能见度观测中的应用

能见度观测和准确预报对于气象学至关重要，需要密集的观测站网络。本文研究了使用静态摄像机进行物体检测和可见性确定的图像处理技术。它提出了一种包括图像预处理、地标识别和能见度估计的综合方法，反映了专业气象观测员的观测过程。这项研究使用 k 最近邻机器学习方法在六个地点验证了能见度观测程序，其中四个地点在捷克共和国，一个在美国，一个在德国。通过将我们的结果与专业观察进行比较，本文证明了所提出的方法对于操作应用的适用性，特别是在雾天和低能见度条件下。