Convective cold pools (CPs) are known to mediate the interaction between convective rain cells and thereby help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has so far been hampered by the lack of relevant large-scale nearsurface data. Unlike numerical studies, where high-resolution near-surface fields of relevant quantities such as virtual temperature and winds are available and frequently used to detect cold pools, observational studies mainly identify CPs based on surface time series. Since research vessels or weather stations measure these time series locally, the characterization of cold pools from observations is limited to regional or station-based studies. To eventually enable studies on a global scale, we here develop and evaluate a methodology for the detection of CPs that relies only on data that (i) is globally available and (ii) has high spatio-temporal resolution. We trained convolutional neural networks to segment CPs in cloud and rainfall fields from high-resolution cloud resolving simulation output. Such data is not only available from simulations, but also from geostationary satellites that fulfill both (i) and (ii). The networks make use of a U-Net architecture, a common choice for image segmentation due to its strength in learning spatial correlations at different scales. Based on cloud and rainfall fields only, the trained networks systematically identify CP pixels in the simulation output. Our methodology may thus open for reliable global CP detection from space-borne sensors. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may offer new insight into the role of CPs in convective organization.