## 1 Introduction  
Landslide susceptibility (LS) statistical modelling requires study areas to be partitioned into a set of mapping units where, based on the matching between predictors (the controlling factors) and outcome (the stable/unstable slope status), the main dataframes required for regression or classification are extracted. Among the different mapping units which can be adopted in LS modelling (e.g., grid cells, slope units and terrain units), square grid cells are largely exploited as they directly replicate the data structure of digital terrain models (DTMs), which constitute the main source layer for the derivation of those covariates, which potentially explain the spatial distribution of landslides (the controlling factors). At the same time, grid cells furnish an objective criterion in choosing the operative mapping unit in LS mapping. However, potential uncoupling between the grid's regular geometry and the randomness of the morphodynamic phenomena could arise and hamper the quality of the susceptibility models. In this sense, a key point in adopting grid-based mapping units is the optimal sizing of the cell by assuming one of the source DTMs as a minimum extension. In fact, the size of the adopted grid cells potentially influences the final susceptibility map results that are obtained, either in terms of predictive performance (accuracy and reliability), the geomorphological adequateness of the models or the ease with which maps can be handled by the final users. Reducing the size of the grid cells can result in an increase in model resolution; however, the accuracy of the final susceptibility map depends on optimal coupling between cell dimension and the controlling factor of spatial variability in the so-called diagnostic areas (where we assume we recognise the conditions for the activation of the known calibration phenomena). In this sense, even for small phenomena, instability conditions are frequently captured in spatial domains larger than a very small pixel. It is worth stressing this point, considering the current availability of centimetric DTMs derived from airborne or drone image/LIDAR acquisition. In addition, typical diagnostic areas in regional or large inventories are simply defined as corresponding to Landslide Identification Points (LIPs), located at the highest point along the crown area of the landslide scarps or source areas, which means we can expect to decipher the causes of slope instability in a single cell hosting at least one point. This issue has been explored in a number of papers which have addressed different types of landslides. In particular, [PERSON] et al. [1] focused on the flow type of the Sidney basin by exploiting 2, 5, 10, 15, 20, 25, 30 and 40 m resolutions, concluding that the medium resolutions performed better than the rest. [PERSON] et al. [2] analysed the effect of grid cells' size on debris flows in the Giampilieri basin (Sicily, Italy), focusing on 2, 4, 16 and 32 m resolutions. Between the high performances obtained for all of the models, the 16 m resolution produced slightly better predictions, while the 32 m resolution showed the worst. Similarly, [PERSON] et al. [3] investigated the effect of the raster resolution on susceptibility evaluation for debris and mud flows in the Briga and Giampilieri catchments (Sicily, Italy) considering 10, 20, 30, 40 and 50 m DTMs. The best performances were obtained from the medium resolutions, thus concluding that \"not always an increase of resolution implies an improvement in the susceptibility analysis\". [PERSON] et al. [4] analysed the effect on shallow landslides (Bijar region, Iran) by considering six different raster resolutions (10, 15, 20, 30, 50 and 100 m). In this case, better performances were obtained by employing the minimum tested size (10 m). [PERSON] et al. [5] tested three resolutions (5, 15 and 30 m) by employing a mixed inventory of slides, falls and debris flows triggered by a 2013 earthquake in Lushan, China; once again, the best performance was obtained from the medium resolution (15 m).