This article presents an operational system for the automatic production of high-resolution (HR) large-scale land cover (LC) maps in a fast, efficient, and unsupervised manner. This is based on a scalable and parallelizable tile-based approach, which does not require the collection of new training data. The method leverages the complementary information provided by the existing LC maps and recent acquisitions of HR Earth observation (EO) images to identify map units that have the highest probability of being correctly associated with their labels, and exploit the obtained 'weak' training set to produce an updated HR LC map by classifying the recently acquired EO data. Both steps, performed at tile level, can be implemented on a high-performance computing (HPC) environment, which simultaneously process all required tiles (independently of each other) for the entire study area. The method was tested considering the publicly available 2018 Corine LC map having a minimum mapping unit of 25 ha and the Sentinel-2 images to generate an HR LC map of Italy. The obtained map has a spatial resolution of 10 m and presents the nine major LC types (i.e., 'artificial land,' 'bareland,' 'grassland,' 'cropland,' 'broadleaves,' 'conifers,' 'snow,' 'water,' and 'shrubland'). Validation was performed using the 2018 European Land Use and Coverage Area Frame Survey database made up of in situ data. The overall accuracy achieved for the Northern, Southern, and Central part of Italy and the Italian Islands is 91.29%, 91.63%, 92.21%, and 91.06%, respectively.

A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian Country Case Study / Paris, C.; Gasparella, L.; Bruzzone, L.. - In: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. - ISSN 1939-1404. - 15:(2022), pp. 9146-9159. [10.1109/JSTARS.2022.3209902]

A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian Country Case Study

Paris C.;Bruzzone L.
2022-01-01

Abstract

This article presents an operational system for the automatic production of high-resolution (HR) large-scale land cover (LC) maps in a fast, efficient, and unsupervised manner. This is based on a scalable and parallelizable tile-based approach, which does not require the collection of new training data. The method leverages the complementary information provided by the existing LC maps and recent acquisitions of HR Earth observation (EO) images to identify map units that have the highest probability of being correctly associated with their labels, and exploit the obtained 'weak' training set to produce an updated HR LC map by classifying the recently acquired EO data. Both steps, performed at tile level, can be implemented on a high-performance computing (HPC) environment, which simultaneously process all required tiles (independently of each other) for the entire study area. The method was tested considering the publicly available 2018 Corine LC map having a minimum mapping unit of 25 ha and the Sentinel-2 images to generate an HR LC map of Italy. The obtained map has a spatial resolution of 10 m and presents the nine major LC types (i.e., 'artificial land,' 'bareland,' 'grassland,' 'cropland,' 'broadleaves,' 'conifers,' 'snow,' 'water,' and 'shrubland'). Validation was performed using the 2018 European Land Use and Coverage Area Frame Survey database made up of in situ data. The overall accuracy achieved for the Northern, Southern, and Central part of Italy and the Italian Islands is 91.29%, 91.63%, 92.21%, and 91.06%, respectively.
2022
Paris, C.; Gasparella, L.; Bruzzone, L.
A Scalable High-Performance Unsupervised System for Producing Large-Scale HR Land Cover Maps: The Italian Country Case Study / Paris, C.; Gasparella, L.; Bruzzone, L.. - In: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING. - ISSN 1939-1404. - 15:(2022), pp. 9146-9159. [10.1109/JSTARS.2022.3209902]
File in questo prodotto:
File Dimensione Formato  
HPC_LC_Maps.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 1.72 MB
Formato Adobe PDF
1.72 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/364408
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 5
social impact