Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map

Abstract

This letter introduces the new European Settlement Map (ESM) production workflow, presents some indicatory results, and discusses the validation process. Unlike the previous ESM versions, the built-up (BU) extraction is realized through supervised learning (not only by means of image filtering and processing techniques) based on textural and morphological features. Input data are the Copernicus very-high-resolution image collection for the reference year 2015 coming from a variety of sensors. The workflow is fully automated, and it does not include any postprocessing. For the first time, a new layer containing nonresidential buildings was derived by using only remote sensing imagery and training data. The produced BU map is delivered at 2-m-pixel resolution (level-1 layer), while the residential/nonresidential layer (level 2) is delivered at 10-m spatial resolution. More than 46 000 scenes were processed, and around 6 million km2 of the European territory was mapped. The workflow was executed on the JRC Big Data platform. The validation showed a balanced accuracy of 0.81 and 0.91 for level 1 and 2 layers, respectively, and 0.71 for only the nonresidential layer.