We introduce a multi-branch Deep Learning architecture that allows for the extraction of multi-scale features. Exploiting the data multi-modality structure through the combined use of various feature extractors provides high performance on data fusion tasks. Furthermore, the representation of the multitemporality of the data using sensor-specific 3D convolutions with custom kernel size extracts temporal features at an early computation stage. Our methodology allows reaching performance up to 0.8876 F1 Score on the development phase dataset and around 0.8798 on the test phase dataset. Finally, we demonstrate the contribution of each sensor to the prediction task with the design of data-focused experiments.