A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation

• URL: http://arxiv.org/abs/2606.00676v1
• Date: Sat, 30 May 2026 11:20:29 GMT
• Title: A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation
• Abstract summary: This work presents a pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel-level annotations.<n>The dataset contains 67,337 patches from five European countries and uses a reduced taxonomy of ten crop classes plus background.<n>A four-level U-Net with Group Normalization was trained using 10 Sentinel-2 spectral bands and a composite loss combining class-weighted cross-entropy and Dice loss.
• Score: 78.66324246922831
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents a configurable pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel-level annotations. The workflow transforms heterogeneous vector crop annotations into aligned multispectral image--mask pairs through label harmonization, Sentinel-2 product selection, spatial alignment, rasterization, patch extraction, quality filtering, and class-aware sample selection. The generated dataset contains 67,337 patches from five European countries and uses a reduced taxonomy of ten crop classes plus background. A four-level U-Net with Group Normalization was trained using 10 Sentinel-2 spectral bands and a composite loss combining class-weighted cross-entropy and Dice loss. On the internal EuroCrops-based test split, the model achieved a mean Intersection over Union (mIoU) of 0.7665, a pixel accuracy of 0.8693, and a mean class accuracy of 0.9072. Compared with spectral and spatial-context Random Forest baselines, the U-Net showed the importance of learned multi-scale spatial representations for crop segmentation. External evaluation was performed on unseen Belgian EuroCrops subsets, DACIA5, and PASTIS. The results show a clear performance gap under external and cross-dataset evaluation, especially for benchmarks with different taxonomies, annotation protocols, spatial coverage, or temporal organization. The model transfers more reliably to dominant and taxonomically aligned classes such as maize and wheat, while performance remains limited for several minority classes and for the adapted single-date PASTIS setting. These findings highlight both the potential and the limitations of using EuroCrops-derived supervision for Sentinel-2 crop segmentation under realistic domain shifts.

Related papers

• PhenoStitch: Training-Free Panoptic Crop Mapping from Satellite Image Time Series [2.1010398528585825]
  PhenoStitch is a panoptic crop-mapping pipeline that requires no task-specific training.<n>It achieves 20.0 crop mIoU, 76.2 segmentation quality, and 6.2 panoptic quality on PASTIS-R under 5-fold, 3-seed evaluation.
  arXiv  Detail & Related papers  (2026-08-01T21:11:01Z)
• FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry [52.232826445242644]
  FoundationGeo bridges relative and metric prediction via spatial calibration and principled data design.<n>Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3.<n>Stage 2 moves beyond global scaling by introducing pixel-wise calibration fields for metric estimation.
  arXiv  Detail & Related papers  (2026-07-13T14:10:01Z)
• FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
  FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
  arXiv  Detail & Related papers  (2026-05-27T08:55:54Z)
• PRUE: A Practical Recipe for Field Boundary Segmentation at Scale [50.194423500109025]
  We conduct the first systematic evaluation of segmentation and geospatial foundation models (GFMs) for global field boundary delineation.<n>A U-Net semantic segmentation model outperforms instance-based and GFM alternatives on a suite of performance and deployment metrics.<n>Our approach provides a practical framework for reliable, scalable, and reproducible field boundary delineation across model design, training, and inference.
  arXiv  Detail & Related papers  (2026-03-28T02:47:46Z)
• SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learning [0.7799711162530713]
  Current ML approaches either require impractical data aggregation or fail due to class imbalance.<n>We introduce SCOPE, a coreset framework for federated data that filters anomalies and adaptively prunes redundant data to long-tail skew.<n>SCOPE yields competitive global accuracy and robust convergence, all while achieving exceptional efficiency with a 128x to 512x reduction in uplink bandwidth.
  arXiv  Detail & Related papers  (2026-03-13T13:24:12Z)
• Split Matching for Inductive Zero-shot Semantic Segmentation [56.47556212515178]
  Zero-shot Semantic (ZSS) aims to segment categories that are not annotated during training.<n>We propose Split Matching (SM), a novel assignment strategy that decouples Hungarian matching into two components.<n>SM is the first to introduce decoupled Hungarian matching under the inductive ZSS setting, and achieves state-of-the-art performance on two standard benchmarks.
  arXiv  Detail & Related papers  (2025-05-08T07:56:30Z)
• CerraData-4MM: A multimodal benchmark dataset on Cerrado for land use and land cover classification [5.503948543987285]
  CerraData-4MM is a dataset combining Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Imagery (MSI) with 10m spatial resolution.<n>The dataset includes two hierarchical classification levels with 7 and 14 classes, respectively, focusing on the diverse Bico do Papagaio ecoregion.<n>We highlight CerraData-4MM's capacity to benchmark advanced semantic segmentation techniques by evaluating a standard U-Net and a more sophisticated Vision Transformer (ViT) model.
  arXiv  Detail & Related papers  (2025-01-31T15:57:17Z)
• EuroCropsML: A Time Series Benchmark Dataset For Few-Shot Crop Type Classification [9.670182163018804]
  EuroCropsML is an analysis-ready remote sensing machine learning dataset for time series crop type classification of agricultural parcels in Europe.<n>Based on the open-source EuroCrops collection, EuroCropsML is publicly available on Zenodo.
  arXiv  Detail & Related papers  (2024-07-24T17:50:54Z)
• Large Scale Legal Text Classification Using Transformer Models [0.0]
  We study the performance of transformer-based models in combination with strategies such as generative pretraining, gradual unfreezing and discriminative learning rates. WeLEX quantify the impact of individual steps, such as language model fine-tuning or gradual unfreezing in an ablation study.
  arXiv  Detail & Related papers  (2020-10-24T11:03:01Z)
• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection [85.53263670166304]
  One-stage detector basically formulates object detection as dense classification and localization. Recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization.
  arXiv  Detail & Related papers  (2020-06-08T07:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.