A Decade of Wheat Mapping for Lebanon
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Abstract (arXiv)
Wheat accounts for approximately 20% of the world's caloric intake, making it a vital component of global food security. Given this importance, mapping wheat fields plays a crucial role in enabling various stakeholders, including policy makers, researchers, and agricultural organizations, to make informed decisions regarding food security, supply chain management, and resource allocation. In this paper, we tackle the problem of accurately mapping wheat fields out of satellite images by introducing an improved pipeline for winter wheat segmentation, as well as presenting a case study on a decade-long analysis of wheat mapping in Lebanon. We integrate a Temporal Spatial Vision Transformer (TSViT) with Parameter-Efficient Fine Tuning (PEFT) and a novel post-processing pipeline based on the Fields of The World (FTW) framework. Our proposed pipeline addresses key challenges encountered in existing approaches, such as the clustering of small agricultural parcels in a single large field. By merging wheat segmentation with precise field boundary extraction, our method produces geometrically coherent and semantically rich maps that enable us to perform in-depth analysis such as tracking crop rotation pattern over years. Extensive evaluations demonstrate improved boundary delineation and field-level precision, establishing the potential of the proposed framework in operational agricultural monitoring and historical trend analysis. By allowing for accurate mapping of wheat fields, this work lays the foundation for a range of critical studies and future advances, including crop monitoring and yield estimation.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 15 Apr 2025.
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6 of 15 references.
References · 6
- 2021Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networkscited by 141
- 2023ViTs for SITS: Vision Transformers for Satellite Image Time Seriescited by 55
- 2021Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Seriescited by 49
- 2024Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentationcited by 23
- 2022Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervisioncited by 21
- 2023Empirical Study of PEFT techniques for Winter Wheat Segmentationcited by 3
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