1. Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map
- Author
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Kenji Ose, Vasileios Syrris, Pierre Soille, Thomas Kemper, Martino Pesaresi, Filip Sabo, Panagiotis Politis, Christina Corbane, European Commission - Joint Research Centre [Ispra] (JRC), ARHS DEVELOPMENTS ITALIA MILAN ITA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), ARHS DEVELOPMENTS BELVAUX LUX, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), and Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
- Subjects
BUILT-UP (BU) ,SYMBOLIC MACHINE LEARNING ,Big data ,Feature extraction ,0211 other engineering and technologies ,Cloud computing ,02 engineering and technology ,computer.software_genre ,Electrical and Electronic Engineering ,Layer (object-oriented design) ,HUMAN SETTLEMENTS ,Image resolution ,021101 geological & geomatics engineering ,NONRESIDENTIAL BUILDINGS ,business.industry ,Supervised learning ,Process (computing) ,MORPHOLOGICAL FEATURES ,BUILDING TYPOLOGY ,Geotechnical Engineering and Engineering Geology ,Workflow ,[SDE]Environmental Sciences ,Data mining ,MACHINE LEARNING ,GLOBAL HUMAN SETTLEMENT LAYER (GHSL) ,business ,computer - Abstract
International audience; 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.
- Published
- 2020
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