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Scientific exploitation of the ever increasing volumes of astronomical datarequires efficient and practical methods for data access, visualisation, andanalysis. Hierarchical sky tessellation techniques enable a multi-resolutionapproach to organising data on angular scales from the full sky down to theindividual image pixels. Aims. We aim to show that the Hierarchical progressivesurvey (HiPS) scheme for describing astronomical images, source catalogues, andthree-dimensional data cubes is a practical solution to managing large volumesof heterogeneous data and that it enables a new level of scientificinteroperability across large collections of data of these different datatypes. Methods. HiPS uses the HEALPix tessellation of the sphere to define ahierarchical tile and pixel structure to describe and organise astronomicaldata. HiPS is designed to conserve the scientific properties of the dataalongside both visualisation considerations and emphasis on the ease ofimplementation. We describe the development of HiPS to manage a large number ofdiverse image surveys, as well as the extension of hierarchical image systemsto cube and catalogue data. We demonstrate the interoperability of HiPS andMulti-Order Coverage (MOC) maps and highlight the HiPS mechanism to providelinks to the original data. Results. Hierarchical progressive surveys have beengenerated by various data centres and groups for ~200 data collectionsincluding many wide area sky surveys, and archives of pointed observations.These can be accessed and visualised in Aladin, Aladin Lite, and otherapplications. HiPS provides a basis for further innovations in the use ofhierarchical data structures to facilitate the description and statisticalanalysis of large astronomical data sets.

Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes