A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

Abstract

In this work, we introduce Sen4AgriNet, a Sentinel-2-based time series multicountry 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 standardize a new crop type taxonomy across Europe that addresses common agriculture policy (CAP) needs, based on the Food and Agriculture Organization (FAO) indicative crop classification scheme. Sen4AgriNet is the only multicountry, multiyear 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 subdatasets 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. 1

AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning

“…The first challenge in creating AgriSen-COG was label harmonization, as each country uses different naming conventions for their crops, and they even differ from year to year. As in [14], we followed the official FAO crop naming conventions. The second challenge involved the discovery of potential anomalous labels, as the LPIS data are created based on farmers' declarations.…”

“…Sen4AgriNet [14] is also a crop classification dataset based on Sentinel-2 Level-1C and LPIS data. The dataset uses the FAO ICC [15] classification for aggregating the LPIS information from France and Catalonia.…”

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

“…You can find the recommendation results in Figure 21 and Figure 22 . The remote sensing resources depicted in Figure 21 and Figure 22 are sourced from the literature [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ].…”

CaBuAr: California burned areas dataset for delineation [Software and Data Sets]

“…In the EO domain, different public datasets are available to the research community tackling different problems, such as flood delineation [12], crop classification and segmentation [23] but, to the best of our knowledge, only two public datasets are available for the burned area delineation problem covering some countries in Europe [24] and Indonesia [25]. The dataset proposed in this paper collects both pre-and post-fire Sentinel-2 L2A data from California forest fires, limiting seasonal and phenological differences between the two acquisitions as explained in the following paragraphs.…”