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We decided to use ML models, which especially relate to convolution neural networks (CNN), due to the strong results they have shown in multiple science fields. The convolutional networks have been mainly used for visual processing. For example, Yu et al. [12] created a state-of-the-art CNN to identify cracks that develop on concrete structures. The precision of their networks was greater than 90%, which shows their capabilities in civil engineering, and their potential in many other fields and applications. One of those applications is shown in Javanmardi et al. [13]. They created a method which reduces the limitations of CNNs when captioning images. This has increased the attention on the advantages of CNNs when identifying and classifying objects, and it also sheds light regarding the capability of the network when forecasting. One interesting example relates to air quality, which has been diversely studied. For example, Kalajdjieski et al. [14] show that CNNs are able to predict the amount of pollution from satellite images, without using raw time series. This is certainly of great utility for places without ground stations. Another example is the work of Kabir et al. [15], which also used satellite images to determine the spatial distribution of pollution. These works, and many others, some of which are referenced in this paper, have built the basis for the application of CNNs to different environmental problems, which can be solved with satellite images and/or time-series of data, as the first part of this paper shows. 2ff7e9595c
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