TY - JOUR
T1 - TimeSen2Crop
T2 - A million labeled samples dataset of Sentinel 2 image time series for crop-type classification
AU - Weikmann, G.
AU - Paris, C.
AU - Bruzzone, Lorenzo
N1 - Funding Information:
Manuscript received March 3, 2021; revised April 6, 2021; accepted April 13, 2021. Date of publication April 19, 2021; date of current version May 24, 2021. This work was supported in part by the European Union’s Horizon 2020 research and innovation programme under Grant 825258. (Corresponding author: Lorenzo Bruzzone.) The authors are with the Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy (e-mail: giulio.weikmann@unitn.it; claudia.paris@unitn.it; lorenzo. bruzzone@ing.unitn.it). Digital Object Identifier 10.1109/JSTARS.2021.3073965
Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers. All rights reserved.
PY - 2021/4/19
Y1 - 2021/4/19
N2 - This article presents TimeSen2Crop, a pixel-based dataset made up of more than 1 million samples of Sentinel 2 time series (TSs) associated to 16 crop types. This dataset, publicly available, aims to contribute to the worldwide research related to the supervised classification of TSs of Sentinel 2 data for crop type mapping. TimeSen2Crop includes atmospherically corrected images and reports the snow, shadows, and clouds information per labeled unit. The provided TSs represent an agronomic year (i.e., period from one year's harvest to the next one for agricultural commodity) ranging from September 2017 to August 2018. To generate the dataset, the publicly available Austrian crop type map based on farmer's declarations has been considered. To ensure the selection of reliable labeled units from the map (i.e., pure pixels correctly associated to their labels), an automatic procedure for the extraction of the training set based on a multitemporal deep learning model has been defined. TimeSen2Crop also includes a TS of Sentinel 2 images acquired in the following agronomic year (i.e., from September 2018 to August 2019). These data are provided with the aim of attract more research activities for solving a typical challenge of the crop type mapping task: Adapting multitemporal deep learning models to different year (domain adaptation). The design of the dataset is described along with a benchmark comparison of deep learning models for crop type mapping.
AB - This article presents TimeSen2Crop, a pixel-based dataset made up of more than 1 million samples of Sentinel 2 time series (TSs) associated to 16 crop types. This dataset, publicly available, aims to contribute to the worldwide research related to the supervised classification of TSs of Sentinel 2 data for crop type mapping. TimeSen2Crop includes atmospherically corrected images and reports the snow, shadows, and clouds information per labeled unit. The provided TSs represent an agronomic year (i.e., period from one year's harvest to the next one for agricultural commodity) ranging from September 2017 to August 2018. To generate the dataset, the publicly available Austrian crop type map based on farmer's declarations has been considered. To ensure the selection of reliable labeled units from the map (i.e., pure pixels correctly associated to their labels), an automatic procedure for the extraction of the training set based on a multitemporal deep learning model has been defined. TimeSen2Crop also includes a TS of Sentinel 2 images acquired in the following agronomic year (i.e., from September 2018 to August 2019). These data are provided with the aim of attract more research activities for solving a typical challenge of the crop type mapping task: Adapting multitemporal deep learning models to different year (domain adaptation). The design of the dataset is described along with a benchmark comparison of deep learning models for crop type mapping.
KW - ITC-CV
UR - https://iris.unitn.it/handle/11572/315283
U2 - 10.1109/JSTARS.2021.3073965
DO - 10.1109/JSTARS.2021.3073965
M3 - Article
VL - 14
SP - 4699
EP - 4708
JO - IEEE Journal of selected topics in applied earth observations and remote sensing
JF - IEEE Journal of selected topics in applied earth observations and remote sensing
SN - 1939-1404
M1 - 9408357
ER -