0]. We provide details on previous observations of gas tracers (H[i]{}, CO etc.) in the complex in Section 4.5.

Convolution and background subtraction
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We convolve the IRAC, MIPS, PACS and SPIRE 250 and 350 [$\mu$m]{} maps to the lowest resolution available, namely that of SPIRE 500 [$\mu$m]{} (FWHM: 36). We used the convolution kernels developed by @Aniano2011, which are rotationally symmetric and optimized to avoid high-frequency numerical noise (induced by the filtering steps when one works in the frequency domain). The LABOCA 870 [$\mu$m]{} map is convolved to the SPIRE 500 [$\mu$m]{} resolution using the low resolution kernel created by G. Aniano from the PSFs of both SPIRE 500 [$\mu$m]{} and [LABOCA]{} (see Section 2.5). For each image, we estimate the background by masking the emission linked with the complex and fitting the distribution of the remaining pixels by a Gaussian. The peak value of this Gaussian is used as a background value per pixel and subtracted from the maps. In the following study, we restrict ourselves to resolved elements with a signal-to-noise superior to 1-$\sigma$ in the SPIRE bands.

A resolved SED modelling
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We use data from 3.6 to 500 [$\mu$m]{} to obtain maps of the dust properties within the star-forming complex. Because the data reduction of the LABOCA data might still lead to removal of some of the faintest diffuse emission, we do not use the 870 [$\mu$m]{} map as a constraint for the resolved modelling. We derive average cold temperatures on a resolved basis using a two-temperature (two modified blackbodies) fitting procedure. We then apply a more realistic model [@Galliano2011] in order to investigate the distribution of the star formation rate, radiation field properties or dust properties (mass, PAH fraction) across the star-forming complex. We describe the two resolved SED fitting procedures in this section and analyse the maps of the physical parameters we obtained in Section 4. In Section 5, we per