Home > Publications database > Sen4Map: Advancing Mapping with Sentinel-2 by Providing Detailed Semantic Descriptions and Customizable Land-Use and Land-Cover Data > print

001     1029392
005     20250203133154.0
024 7 _ |a 10.1109/JSTARS.2024.3435081
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100 1 _ |a Sharma, Surbhi
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245 _ _ |a Sen4Map: Advancing Mapping with Sentinel-2 by Providing Detailed Semantic Descriptions and Customizable Land-Use and Land-Cover Data
260 _ _ |a New York, NY
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520 _ _ |a This paper presents Sen4Map, a large-scale benchmark dataset designed to enhance the capability of generating land-cover maps using Sentinel-2 data. Comprising non-overlapping 64×64 patches extracted from Sentinel-2 time series images, the dataset spans 335,125 geo-tagged 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: (1) a detailed description of the land-cover and land-use properties of each sampled area; (2) independence of scale, as it is associated with reference data collected in-situ by expert surveyors; (3) the ability to test both temporal and spatial classification approaches because of the availability of time series of 64×64 patches associated with each labeled sample; and (4) 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. The dataset and code can be downloaded at: https://datapub.fz-juelich.de/sen4map.
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700 1 _ |a Sedona, Rocco
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700 1 _ |a Riedel, Morris
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700 1 _ |a Cavallaro, Gabriele
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700 1 _ |a Paris, Claudia
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773 _ _ |a 10.1109/JSTARS.2024.3435081
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|y 2024
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