The statistical description of Giant Molecular Cloud (GMC) properties relies heavily on the performance of automatic identification algorithms, which are often seriously affected by the survey design. The algorithm we designed, SCIMES (Spectral Clustering for Interstellar Molecular Emission Segmentation), is able to overcome some of these limitations by considering the cloud segmentation problem in the broad framework of the graph theory. The application of the code on the CO(3-2) High Resolution Survey (COHRS) data allowed for a robust decomposition of more than 12,000 objects in the Galactic Plane. Together with the wealth of Galactic Plane surveys of the recent years, this approach will help to open the door to a future, systematic cataloging of all discrete molecular features of our own Galaxy.
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