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These have lately pulled towards the adoption of optical surface topography measuring instruments to qualify technological surfaces, which are core to being assessed to characterise the product and optimise the manufacturing process. Surface topography measurements performed by optical instruments are known to suffer from disturbances, such as non\u2010measured points and spikes. These are due to complex interactions between the measurand and the instrument and may result in poor measurement quality and biased characterisation results. Currently, the correction of measurement disturbances is carried out by empirical approaches which mostly involve thresholding and interpolations, whose sensitivity is often devolved to the operator. In this work, a Gaussian process regression\u2010based approach is outlined to identify and correct measurement disturbances exploiting spatial correlation properties of the measurements to provide a formal, robust, and univocally defined approach to manage measurement disturbances. The proposed approach, differently from currently available alternatives, is capable of managing at once the different type of disturbances by means of a supervised machine learning technique. The formal and practical advantages of the proposed method are discussed exploiting case studies of industrial interest applied on quasi\u2010flat surfaces to stress the disturbances effect.<\/jats:p>","DOI":"10.1002\/qre.2980","type":"journal-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T10:50:30Z","timestamp":1630493430000},"page":"1501-1518","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Gaussian process regression\u2010based detection and correction of disturbances in surface topography measurements"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2467-0426","authenticated-orcid":false,"given":"Giacomo","family":"Maculotti","sequence":"first","affiliation":[{"name":"Department of Management and Production Engineering Politecnico di Torino  Turin 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