A Dual-Track Curation-and-Classification Framework for Resolving Ground-Truth Label Noise in Operational Sentinel-2 Wheat Area Estimation

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.

Accurate and scalable parcel-level agricultural monitoring remains challenging because satellite Earth Observation alone provides only an overhead perspective of agricultural parcels, while optical observations are further affected by cloud-induced temporal gaps. This paper presents Space2Ground 2.0, a multi-source framework integrating Sentinel-1 SAR and Sentinel-2 multispectral time series with geo-tagged street-level imagery acquired using vehicle-mounted cameras and shared through the Mapillary platform. A largely automated processing pipeline performs semantic filtering, image quality assessment, viewpoint-based parcel association, and dataset refinement, transforming large volumes of crowdsourced imagery into parcel-linked, analysis-ready data. Applied over Cyprus during the 2022 growing season, the pipeline produced a curated dataset of 46,050 annotated street-level images, selected from an initial collection exceeding 900,000 images and linked with satellite information for 8,581 agricultural parcels. The practical value of the dataset was assessed through parcel-level crop classification experiments using both single- and multi-source observations. The results demonstrate that street-level imagery provides complementary fine-scale visual information that enhances classification when integrated with satellite time series. Overall, Space2Ground 2.0 provides an openly available benchmark dataset and a reproducible methodology for multimodal agricultural monitoring, with potential applications in visual verification, reduced reliance on costly field inspections, and data-driven agricultural policy implementation.

The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agriculture are only available at the pixel level. While some high-quality field-level data products exist, they come from parcel registries covering only parts of Europe or from ML-derived products for individual countries. No openly available, globally consistent map of agricultural field boundaries exists to date. Here we present the first global field boundary dataset at 10,m resolution for the years 2024 and 2025, comprising 3.17 billion remote-sensing field polygons (1.62 B in 2024 and 1.55 B in 2025) across 241 countries and territories, produced by applying a U-Net segmentation model trained on the Fields of The World dataset to cloud-free Sentinel-2 mosaics. Validated against ground-truth field boundaries in 24 countries, the map achieved a mean pixel-level recall of 0.85 with 14 countries exceeding 0.90. Evaluation against full-country ground-truth datasets in Austria, Latvia, and Finland yielded F1 scores of 0.89, 0.88, and 0.74, respectively. Because reference data for global validation is inherently incomplete, we accompanied the map with a 500 m confidence layer that identifies regions where predictions are reliable. We release the dataset openly as three global maps: the confidence-thresholded default field boundary dataset, the full unfiltered dataset, and the continuous-valued confidence raster. These maps provide the first globally consistent field-level unit of analysis for crop monitoring, food security, and downstream agricultural science.