The semi-automatic modelling framework, as implemented in the Python code PTS. Only the steps indicated in red cannot completely be automated and thus required manual intervention. At the top of the diagram, the different input sources are displayed. Observed images for the target galaxy can be automatically retrieved from the DustPedia database, but any other image data can also be placed in the modelling environment and included in the procedures. To prepare the images, the 2MASS point sources catalog and the IRSA service provide the pipeline with foreground star positions and dust extinction values respectively. A 2D decomposition of the diffuse stellar component in the galaxy can be automatically retrieved from the S4G catalog. If a Spitzer 3.6 or 4.5 micron image is not available and thus a S4G composition canno be obtained, custom decomposition results can be used instead based on other NIR data. The input maps of the disk components are automatically generated by the pipeline based on well-established prescriptions. A map of the old stellar bulge component, rendered by SKIRT, is used to obtain a map of the old stars in the disk of the galaxy. Crucial in the map making step is the FUV attenuation map. Ingredients to this map are a sSFR colour (the preferential colour is indicated in bold, though other colours can be used depending on the availability and quality of the data) and a TIR map (created by combining multiple MIR/FIR bands without limiting the resolution too much). The maps and 3D bulge description define the geometry of the RT model, together with appropriate scale heights for each of the disk components. The RT model is further constrained with a wavelength grid, an instrument setup (based on the pixelscale of the lowest resolution map), and a dust grid structure (based on the pixelscale of the highest resolution map). This basic RT model definition is supplied to SKIRT, with different normalizations of the stellar and dust components. The ranges of these free parameters are defined around values found with a global SED fit based on the observed photometry. Each simulation that represent a certain set of normalization values is evaluated by creating mock fluxes from the simulated SEDs and datacubes.