Despite significant advances in monitoring of the development of active lava flow fields, many challenges remain. Timely field surveys of active lava flows could improve our understanding of the development of flow fields, but data of sufficient accuracy, spatial extent and repeat frequency have yet to be acquired. Satellite remote sensing of volcanoes is very useful because it can provide data for large areas with a variety of modalities ranging from visible to infra-red and radar. Satellite sensing can also access remote locations and hazardous regions without difficulty. Radar and multispectral satellite sensing data have been shown that can be combined to map heterogeneous lava flows using machine learning techniques, but a robust general model trained with several different lava compositions has to be developed. Here, we propose a robust, automatic approach based on machine learning techniques for analysing open-access satellite data in order to map lava flows in near-real time applicable to different kind of lava with different thermal components (i.e., incandescent, cooling and cooled lava component). We built a neural network model and trained it with a set of satellite images (e.g., Sentinel-1 SAR, Sentinel-2 MSI and Landsat 8 OLI/TIRS) of recent lava flows, and the relative labels of the lava and background regions. In this way, the trained model becomes capable to detect and map lava flows and to classify any new image, when available. The relative output is a segmented image with lava and background classes, obtained without an analysis made by a human operator. This approach allows to segment lava flows with both hot spot and cooling parts, and to recognize lava flows with different characteristics in near-real time. The results obtained during the long sequence of short-lived eruptive events occurred at Mt. Etna (Italy) between 2020 and 2021 are shown.