DOI: 10.5281/zenodo.15395167
title: MT4AFE - A Deep Learning Benchmark Dataset for Agricultural Field Extraction

description: MT4AFE (Multi-Task Learning for Agricultural Field Extraction)

MT4AFE is a dataset composed of high-resolution Sentinel-2 Level-3A satellite imagery and detailed reference data on agricultural fields derived from the French graphical parcel registry (RPG). The dataset includes 132,222 labeled image patches, each measuring 256 × 256 pixels, extracted from multiple Sentinel-2 tiles across France. It covers a wide diversity of landscapes, environmental settings, and agricultural conditions. MT4AFE has been curated to support researchers in the development and evaluation of semantic segmentation deep learning methods for agricultural field delineation. It provides not only pixel-level segmentation labels, but also field boundary annotations and distance maps, making it particularly well-suited for multi-task learning approaches.

Dataset Structure Description

The dataset is organized into three main folders corresponding to the years  [2021, 2022, and 2023]. 

Each folder contains two subfolders:



S2_FE: Contains the Sentinel-2 Level-3A patches.

LABELS_FE: Contains the corresponding label patches.


1. Training and Validation Data [2021, 2022]

The folders for 2021 and 2022 are intended for model training and validation.Each of their subfolders (S2_FE and LABELS_FE) is further divided into three subsets:



training (~ 75% of the data per site)

validation (~ 15%)

test (~10%)


Both the 2021 and 2022 datasets cover the same geographic sites, but each year uses different Sentinel-2 acquisitions and the corresponding labels. 

2. Generalization Data [2023]

The 2023 folder is designed for spatio-temporal generalization testing of the trained models. It contains data from different geographic sites not included in the training, validation or test phases. This folder is not split into training, validation, or test subsets.

3. Data Format

All data are provided in GeoTIFF format, ensuring geospatial compatibility and easy integration with remote sensing workflows.

Each Sentinel-2 image patch contains five channels in the following order:[Red (R), Green (G), Blue (B), Near-Infrared (IR), and NDVI]

The corresponding label patches include three masks in the following order:[Field extent masks, Field boundaries, Distance maps]

4. File Naming Convention

Each Sentinel-2 image patch and its corresponding label patch follow a consistent naming format :



First part: Unique identifier (ID).

Second part: Data source (S2 for Sentinel-2 images or Labels for labels masks).

Middle parts: Tile ID and acquisition date.

Last part: Dataset split (_train.TIF, _valid.TIF, _test.TIF).


Example:



1_S2_T30TYP_20210315_train.TIF — Sentinel-2 image patch

1_Labels_T30TYP_20210315_train.TIF — Corresponding label patch


5. Statistics Files

The dataset includes four statistical summary files (.txt format), providing key information about the Sentinel-2 data before image patching: one file for each year (2021, 2022, 2023) and one for the combined 2021–2022 dataset. 

Each file contains the mean and standard deviation values for five bands/features [Red (R), Green (G), Blue (B), Near-Infrared (IR), NDVI]

These statistics are calculated prior to patch extraction, based exclusively on the training subset, ensuring no data leakage between the training and validation datasets. This allows for consistent standardization of patches during model training.

In addition, each file includes information calculated prior to patch extraction, derived from the full label extent masks (i.e., training, validation, and test masks), including:



Total number of pixels

Count and percentage of class 0 (non-field)

Count and percentage of class 1 (field)


6. Dataset Summary

Data Partition Number of Patches






Data Partition
Number of Patches




Training
93,198


Validation
18,252


Test
12,128


Generalization
8,644


Total
132,222






 

7. Notes 

Note 1: For more details about the data preparation and differences between years, please refer to the original article associated with this dataset.

 

Note 2: NDVI was computed from surface reflectances following the standard formula, with a small constant (0.01) added to the red band to reduce sensitivity to atmospheric correction noise. This approach is based on the recommendation by Hagolle (CESBIO), detailed here.

 



8. Citation 

Please cite the following letter when publishing work derived from this dataset.

IEEE style : G. Amin, O. Hagolle, and V. Demarez, “MT4AFE: A Deep Learning Benchmark Dataset for Agricultural Field Extraction,” IEEE Geosci. Remote Sens. Lett., vol. 22, pp. 1–5, 2025, doi: 10.1109/LGRS.2025.3613688.

Elsevier/Harvard style : Amin, G., Hagolle, O., Demarez, V., 2025. MT4AFE: A Deep Learning Benchmark Dataset for Agricultural Field Extraction. IEEE Geosci. Remote Sens. Lett. 22, 1–5. https://doi.org/10.1109/LGRS.2025.3613688