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

Abstract

This paper presents an operational system for the automatic production of 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 existing LC maps and recent acquisitions of HR Earth Observation (EO) images to: (i) identify map units that have the highest probability of being correctly associated with their labels, and (ii) 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 Land Cover (CLC) Map having a minimum mapping unit of 25Ha and the Sentinel-2 images to generate a 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 (LUCAS) database made up of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> data. The Overall Accuracy (OA) achieved for the Northern, Southern and Central part of Italy and Italian Islands are 91.29%, 91.63%, 92.21% and 91.06%, respectively.