# BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding

## TL;DR

BigEarthNet is a large-scale, multi-label Sentinel-2 image archive that significantly enhances remote sensing image understanding and classification accuracy through deep learning.

## Contribution

It introduces the BigEarthNet, a new extensive multi-label Sentinel-2 dataset, and demonstrates its effectiveness for improving land-cover classification with CNNs.

## Key findings

- CNN trained on BigEarthNet outperforms ImageNet-pretrained models
- BigEarthNet's size enables better deep learning model training
- Multi-label annotations improve land-cover classification accuracy

## Abstract

This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10m bands; ii) 60x60 pixels for 20m bands; and iii) 20x20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes the properties of the BigEarthNet. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives.