The analyses of gamma-ray spectra, i.e. the identification and quantification of the different radionuclides observed, continuous to be an active field of research. Traditionally, this is done by the analyses of the full energy peaks observed in the spectrum, for identification sometimes in combination with other spectral features, such as Compton edges, sum peaks, etc… . Depending on the energy resolution of the detector system, overlapping peaks can be an important challenge in the analyses of the spectra, especially when many radionuclides are observed and spectra become complex. Also spectra with low statistics, influenced by background, are a challenge for analyses, including the determination of a Minimal Detectable Activity in case of non-detection. With the introduction of Artificial Intelligence (AI) techniques, such as artificial neural networks, it has been demonstrated that these techniques can be very interesting in relation with spectral analyses [1,2]. Because such machine learning algorithms use abstract features of the spectrum, as for example the shape of overlapping peaks and the Compton continuum, they seem a natural choice for analyzing radionuclide mixtures or the presence of artificial radionuclides in a natural background. This last situation is something typically encountered in environmental measurements and especially when the measurement time is limited.
Radiological measurements using UAVs or drones, collect in general spectra for short acquisition times to allow, if the radiological information is combined with the location, the construction of radiological maps (e.g. contamination maps, the localization of source(s), ...). Acquisition times are often limited to a few seconds (1 - 5 s). Even when combining multiple spectra, the total time in which the detector is near the source during a UAV survey can be very limited. Consequently, identification and also quantification of artificial radionuclides in such surveys is a challenging task. In this master thesis we will explore the use of artificial neural networks to identify single radionuclides in typical UAV surveys collected with a CsI scintillator detector under different natural backgrounds.
 V. Pilato, F. Tola, J.M. Martinez, M. Huver, Application of neural networks to quantitative spectrometry analysis, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 422, Issues 1–3, 1999, Pages 423-427, ISSN 0168-9002, https://doi.org/10.1016/S0168-9002(98)01110-3.
 M. Kamuda, J. Stinnett and C. J. Sullivan, "Automated Isotope Identification Algorithm Using Artificial Neural Networks," in IEEE Transactions on Nuclear Science, vol. 64, no. 7, pp. 1858-1864, July 2017, doi: 10.1109/TNS.2017.2693152.