TITLE: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning 

ABSTRACT: In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi
country benchmark dataset, tailored for agricultural monitoring applications
with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer
declarations collected via the Land Parcel Identification System (LPIS) for
harmonizing country wide labels. These declarations have only recently been
made available as open data, allowing for the first time the labeling of
satellite imagery from ground truth data. We proceed to propose and standardise
a new crop type taxonomy across Europe that address Common Agriculture Policy
(CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative
Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year
dataset that includes all spectral information. It is constructed to cover the
period 2016-2020 for Catalonia and France, while it can be extended to include
additional countries. Currently, it contains 42.5 million parcels, which makes
it significantly larger than other available archives. We extract two
sub-datasets to highlight its value for diverse Deep Learning applications; the
Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD
capitalizes zonal statistics of each parcel, thus creating a powerful
label-to-features instance for classification algorithms. On the other hand,
PAD structure generalizes the classification problem to parcel extraction and
semantic segmentation and labeling. The PAD and OAD are examined under three
different scenarios to showcase and model the effects of spatial and temporal
variability across different years and different countries.
In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi
country benchmark dataset, tailored for agricultural monitoring applications
with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer
declarations collected via the Land Parcel Identification System (LPIS) for
harmonizing country wide labels. These declarations have only recently been
made available as open data, allowing for the first time the labeling of
satellite imagery from ground truth data. We proceed to propose and standardise
a new crop type taxonomy across Europe that address Common Agriculture Policy
(CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative
Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year
dataset that includes all spectral information. It is constructed to cover the
period 2016-2020 for Catalonia and France, while it can be extended to include
additional countries. Currently, it contains 42.5 million parcels, which makes
it significantly larger than other available archives. We extract two
sub-datasets to highlight its value for diverse Deep Learning applications; the
Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD
capitalizes zonal statistics of each parcel, thus creating a powerful
label-to-features instance for classification algorithms. On the other hand,
PAD structure generalizes the classification problem to parcel extraction and
semantic segmentation and labeling. The PAD and OAD are examined under three
different scenarios to showcase and model the effects of spatial and temporal
variability across different years and different countries.
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
http://arxiv.org/abs/2204.00951v2