Large geophysical datasets are produced routinely during airborne surveys. The Spatially Constrained Inversion (SCI) is capable of inverting these datasets in an efficient and effective way by using a 1D forward modeling and, at the same time, enforcing smoothness constraints between the model parameters. The smoothness constraints act both vertically within each 1D model discretizing the investigated volume and laterally between the adjacent soundings. Even if the traditional, smooth SCI has been proven to be very successful in reconstructing complex structures, sometimes it generates results where the formation boundaries are blurred and poorly match the real, abrupt changes in the underlying geology. Recently, to overcome this problem, the original (smooth) SCI algorithm has been extended to include sharp boundary reconstruction capabilities based on the Minimum Support regularization. By means of minimization of the volume where, the spatial model variation is non-vanishing (i.e., the support of the variation), sharp-SCI promotes the reconstruction of blocky solutions. In this paper, we apply the novel sharp-SCI method to different types of airborne electromagnetic datasets and, by comparing the models against other geophysical and geological evidences, demonstrate the improved capabilities of in reconstructing sharp features.