# Cloud Detection and Tracking Based on Object Detection with Convolutional Neural Networks  
[PERSON]  
1 CIEMAT, Plataforma Solar de Almeria (PSA), 04200 Almeria, Spain; [EMAIL_ADDRESS] (J.A.C. ); [PERSON](J.F.-R.); antorino.avila@ciemates (A.A.-M.); [EMAIL_ADDRESS] (D.-C.A.-P)  
[PERSON]  
1 CIEMAT, Plataforma Solar de Almeria (PSA), 04200 Almeria, Spain; [EMAIL_ADDRESS] (J.A.C. ); [PERSON](J.F.-R.); antorino.avila@ciemates (A.A.-M.); [PERSON]S] (D.-C.A.-P)  
[PERSON]  
1 CIEMAT, Plataforma Solar de Almeria (PSA), 04200 Almeria, Spain; [EMAIL_ADDRESS] (J.A.C. ); [PERSON](J.F.-R.); antorino.avila@ciemates (A.A.-M.); [PERSON]S] (D.-C.A.-P)  
[PERSON]  
3 Deutsches Zentrum fur Luft-und Raumfahrt (DLR), Institute of Solar Research, 04005 Almeria, Spain; [EMAIL_ADDRESS] (B.N. ); [EMAIL_ADDRESS] (Y.F.) [PERSON]  
4 Centre de Calcul de  
based on artificial neural networks are needed to overcome the constraints and drawbacks of traditional algorithms [7; 8; 10]. Cloud detection methods are classified into traditional and smart methods. Among the traditional methods, threshold-based, time differentiation, and statistics methods can be found [8]. The smart methods include convolutional neural networks (CNN), simple linear iterative clustering, and semantic segmentation algorithms. The majority of works rely on traditional approaches; however, recent articles show that this trend might be shifting towards smart approaches [9]. Smart methods have proved to be more efficient on cloud detection tasks than traditional ones. Cloud detection and classification are usually a first step in solar forecasting/ nowcasting [10; 11]. As an example of an early step, a paper with a quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term direct normal irradiance (DNI) forecasts was published using four different computer vision methods to detect clouds [12]. Later, a new computer vision method was proposed using an enhanced clustering algorithm to track the clouds and predict relevant events based on all-sky images, which can deal with the nature of the variable appearance of clouds [13]. This new computer vision method showed that the proposed method can substantially enhance the accuracy of solar irradiance nowcasting methods. Recently, color space operations and various image segmentation methods were investigated to improve the visual contrast of the cloud component and a novel approach to calculate cloud cover under any illumination conditions for short-term irradiance forecasting was presented with a positive linear correlation between cloud fraction and real-time irradiance data [11]. Regarding smart methods, a recent study proposed a deep learning model suitable for cloud detection based on a U-Net network and an attention mechanism for cloud detection for satellite imagery [14]. The results achieved were excellent and show that this network architecture has great potential for applications in satellite image processing. Furthermore, another proposed approach is an integrated cloud detection and removal framework using cascade CNN, which provides accurate cloud and shadow masks and repaired satellite imagery. One CNN was developed for detecting clouds and shadows from a satellite image and a second CNN was used for the cloud removal and missing-information reconstruction. Experiments showed that the proposed framework can simultaneously detect and remove the clouds and shadows from the images and the detection accuracy surpassed several recent cloud detection methods [15]. In [10], the capacity of a CNN to identify the presence of clouds, without ancillary data and at relatively high temporal resolution, was demonstrated. Recently, a cloud detection method using a multifeature embedded learning support vector machine to address cloud coverage occupying the channel transmission bandwidth was presented [16]. Experimental results demonstrate that the proposed method [16] can detect clouds with great accuracy and robustness.