  This paper presents BigEarthNet that is a large-scale Sentinel-2
multispectral image dataset with a new class nomenclature to advance deep
learning (DL) studies in remote sensing (RS). BigEarthNet is made up of 590,326
image patches annotated with multi-labels provided by the CORINE Land Cover
(CLC) map of 2018 based on its most thematic detailed Level-3 class
nomenclature. Initial research demonstrates that some CLC classes are
challenging to be accurately described by considering only Sentinel-2 images.
To increase the effectiveness of BigEarthNet, in this paper we introduce an
alternative class-nomenclature to allow DL models for better learning and
describing the complex spatial and spectral information content of the
Sentinel-2 images. This is achieved by interpreting and arranging the CLC
Level-3 nomenclature based on the properties of Sentinel-2 images in a new
nomenclature of 19 classes. Then, the new class-nomenclature of BigEarthNet is
used within state-of-the-art DL models in the context of multi-label
classification. Results show that the models trained from scratch on
BigEarthNet outperform those pre-trained on ImageNet, especially in relation to
some complex classes including agriculture, other vegetated and natural
environments. All DL models are made publicly available at
http://bigearth.net/#downloads, offering an important resource to guide future
progress on RS image analysis.
