Journal Article

FZJ-2024-05100

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Sen4Map: Advancing Mapping with Sentinel-2 by Providing Detailed Semantic Descriptions and Customizable Land-Use and Land-Cover Data

Sharma, S.FZJ* ; Sedona, R.FZJ* ; Riedel, M.FZJ* ; Cavallaro, G. (Corresponding author)FZJ* ; Paris, C.

2024
IEEE New York, NY

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Please use a persistent id in citations: doi:10.1109/JSTARS.2024.3435081  doi:10.34734/FZJ-2024-05100

Abstract: 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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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. ADMIRE - Adaptive multi-tier intelligent data manager for Exascale (956748) (956748)
3. Verbundprojekt: ADMIRE - Adaptives Datenmanagement für das Exascale-Computing (16HPC008) (16HPC008)
4. EUROCC-2 (DEA02266) (DEA02266)

Appears in the scientific report 2024

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