AUTOMATIC NON-RESIDENTIAL BUILT-UP MAPPING OVER NATIONAL EXTENTS WITH A SENTINEL-2 IMAGE SEGMENTATION MODEL TRAINED WITH ANCILLARY CENSUS DATA
Keywords: training data, land use, disaster management, convolutional neural networks
Abstract. Information regarding the residential status of the built-area is used within several contexts such as disaster management, urban and regional planning, among others. Currently such non-residential built-up information can be extracted for most of Europe from Land Use/Land Cover maps such as CORINE Land Cover (CLC) and Urban Atlas (UA) by harmonizing the class nomenclature into a residential/non-residential nomenclature. However, these have update cycles of several years given their usually costly and lengthy production, which also relies on visual interpretation of ancillary datasets. Given these limitations many methods have been proposed to increase the thematic detail of the built-up environment. More recently, these methods often rely on ancillary datasets such as, e.g., social media and mobile phone networks metadata, which may not be readily available in many areas. In this paper we propose a framework to map non-residential built-up areas by training an image segmentation model with national census information and Sentinel-2 imagery. The non-residential map coming from the segmentation model was compared with public pan-European maps and both of their quality assessed against UA 2018. The results show that using census data to automatically generate training data for a Sentinel-2 image segmentation model of non-residential built-up improves the mapping of non-residential areas when compared with the existing datasets available for most of Europe.