Flowchart of our active learning method. First, the algorithm is initialised only with labelled training data, the CNN is trained, and unlabelled spectra are classified. Then, uncertainty sampling selects the spectra that the network is least certain for. Finally, these spectra are labelled by an oracle, are added to the training set, and a new training iteration starts. When the performance is satisfactory, samples classified into target classes are taken as candidates and are extended with samples classified into target classes by the oracle.