Sen4Map: Advancing Mapping With Sentinel-2 by Providing Detailed Semantic Descriptions and Customizable Land-Use and Land-Cover Data

This article presents Sen4Map, a large-scale benchmark dataset designed to enhance the capability of generating land-cover maps using Sentinel-2 data. Comprising nonoverlapping
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patches extracted from Sentinel-2 time series images, the dataset spans 335 125 geotagged locations across the European Union. These locations are associated with detailed land-cover and land-use information gathered by expert surveyors in 2018. Unlike most existing large datasets available in the literature, the presented database provides: first, a detailed description of the land-cover and land-use properties of each sampled area; second, independence of scale, as it is associated with reference data collected in situ by expert surveyors; third, the ability to test both temporal and spatial classification approaches because of the availability of time series of
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patches associated with each labeled sample; and fourth, samples were collected following a stratified random sample design to obtain a statistically representative spatial distribution of land-cover classes throughout the European Union. To showcase the properties and challenges offered by Sen4Map, we benchmarked the current state-of-the-art land-cover classification approaches.