TY  - JOUR
AU  - Sharma, Surbhi
AU  - Sedona, Rocco
AU  - Riedel, Morris
AU  - Cavallaro, Gabriele
AU  - Paris, Claudia
TI  - Sen4Map: Advancing Mapping with Sentinel-2 by Providing Detailed Semantic Descriptions and Customizable Land-Use and Land-Cover Data
JO  - IEEE journal of selected topics in applied earth observations and remote sensing
VL  - 17
SN  - 1939-1404
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2024-05100
SP  - 13893 - 13907
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001294364400012
DO  - DOI:10.1109/JSTARS.2024.3435081
UR  - https://juser.fz-juelich.de/record/1029392
ER  -