Digital classification of cloud masses from weather imagery using machine learning algorithms



loud mass classification, machine learning algorithms, weather images, decision trees, support vector machines, random forests


Accurate identification of precipitating clouds is a challenging task. In the present work, Support Vector Machines, Decision Trees and Random Forests algorithms were applied to discriminate between precipitating clouds and non-precipitating clouds from a satellite weather image GOES-13 covering the Colombian territory. The objective of this study was to evaluate the performance of machine learning (ML) algorithms for digital classification of cloud masses in terms of thematic accuracy classification using the conventional Mahalanobis algorithm as benchmark. Results show that ML algorithms provide more accurate classification of cloud masses than conventional algorithms. The best accuracy was obtained using Random Forests (RF), with an overall thematic accuracy of 97%. Furthermore, the classification obtained with the RF algorithm was compared pixel-to-pixel with NASA Tropical Rainfall Measurement Mission (TRMM) rainfall estimates, obtaining an overall accuracy of 94%. ML algorithms can therefore be used to improve current precipitating clouds identification methods.

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Author Biographies

Salomón Einstein Ramírez-Fernández, Universidad Distrital Francisco José de Caldas

Curricular Project Master in Information and Communication Sciences, emphasis in Geomatics,
NIDE Research Group, Faculty of Engineering

Iván Alberto Lizarazo-Salcedo, Universidad Distrital Francisco José de Caldas

Curricular Project Master in Information and Communication Sciences, emphasis in Geomatics,
NIDE Research Group, Faculty of Engineering


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How to Cite

Ramírez-Fernández, S. E., & Lizarazo-Salcedo, I. A. (2014). Digital classification of cloud masses from weather imagery using machine learning algorithms. Revista Facultad De Ingeniería Universidad De Antioquia, (73), 43–57. Retrieved from