Parameter Efficient Fine Tuning (PEFT) techniques have recently experienced significant growth and have been extensively employed to adapt large vision and language models to various domains, enabling satisfactory model performance with minimal computational needs. Despite these advances, more research has yet to delve into potential PEFT applications in real-life scenarios, particularly in the critical domains of remote sensing and crop monitoring. The diversity of climates across different regions and the need for comprehensive large-scale datasets have posed significant obstacles to accurately identify crop types across varying geographic locations and changing growing seasons. This study seeks to bridge this gap by comprehensively exploring the feasibility of cross-area and cross-year out-of-distribution generalization using the State-of-the-Art (SOTA) wheat crop monitoring model. The aim of this work is to explore PEFT approaches for crop monitoring. Specifically, we focus on adapting the SOTA TSViT model to address winter wheat field segmentation, a critical task for crop monitoring and food security. This adaptation process involves integrating different PEFT techniques, including BigFit, LoRA, Adaptformer, and prompt tuning. Using PEFT techniques, we achieved notable results comparable to those achieved using full fine-tuning methods while training only a mere 0.7% parameters of the whole TSViT architecture. The in-house labeled data-set, referred to as the Beqaa-Lebanon dataset, comprises high-quality annotated polygons for wheat and non-wheat classes with a total surface of 170 kmsq, over five consecutive years. Using Sentinel-2 images, our model achieved a 84% F1-score. We intend to publicly release the Lebanese winter wheat data set, code repository, and model weights.

翻译：参数高效微调（PEFT）技术近年来发展迅速，被广泛用于将大型视觉和语言模型适配至不同领域，以极少的计算需求实现满意的模型性能。尽管取得了这些进展，但尚未有研究深入探讨PEFT在真实场景中的应用潜力，尤其是在遥感与作物监测这一关键领域。不同地区的气候多样性以及对大规模综合数据集的需求，给跨地理区域和生长季节的作物类型精确识别带来了显著障碍。本研究旨在通过全面探索使用最先进（SOTA）小麦作物监测模型进行跨区域、跨年份分布外泛化的可行性来填补这一空白。本文旨在探索用于作物监测的PEFT方法。具体而言，我们聚焦于将SOTA的TSViT模型适配至冬小麦田块分割任务，这是作物监测与粮食安全的关键任务。该适配过程涉及集成多种PEFT技术，包括BigFit、LoRA、Adaptformer和提示调优。通过使用PEFT技术，我们在仅训练TSViT整体架构0.7%参数的情况下，取得了与全参数微调方法相当的可观结果。内部标注的数据集（称为贝卡-黎巴嫩数据集）包含连续五年、总面积170平方公里的高质量小麦与非小麦类别标注多边形。使用哨兵2号影像，我们的模型达到了84%的F1分数。我们计划公开发布黎巴嫩冬小麦数据集、代码仓库及模型权重。