EAGER: Collaborative Research: Spatiotemporal transfer learning for enabling cross-country and cross-hemisphere in-season crop mapping

EAGER：协作研究：时空迁移学习，用于实现跨国和跨半球的当季作物绘图

• 批准号: 2227961
• 负责人: Diane Cook
• 金额: $ 5万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227961&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Spatiotemporal; transfer

Crop production is a major industry in the United States (U.S.). In 2021, the U.S. grain export accounted for over 40% share of international grain trade. Millions of U.S. farmers depend on international market for living and prosperity. However, the U.S. grain export is not only facing tough competition from other export countries, but also impacted by grain yield in import countries. In order to gain the competitive edge, stakeholders need to know as early as possible where and how many acres each type of crops that have been planted in a growing season around the world so that yield can be estimated, production and demand balance can be assessed, and grain prices can be predicted. This requires generating in-season crop maps of both U.S. and foreign countries. The classic method to generate in-season crop maps needs a large amount of verified information on crops (i.e., ground truths) to train algorithms for classifying in-season satellite remote sensing images. However, it is difficult or even impossible to obtain ground truths in foreign countries, particularly in early season. This study proposes to develop a spatiotemporally transferable machine-learning algorithm which will be trained with U.S. data and applied to in-season satellite remote sensing images of foreign countries for creating the in-season crop maps of the countries. Success of this project will make the in-season crop mapping of foreign countries possible. The project will significantly enhance the competitiveness and profitability of U.S. agriculture, increase the food security of the world, and potentially bring billions-of-dollars economic benefits to U.S. farmers.Satellite remote sensing with ground truth tagging is the current practice for crop mapping. However, it suffers from two problems: 1) Unavailability of ground truth in foreign countries; 2) Spatiotemporal intransferability of trained classifiers. This study will design spatiotemporally transferable learning algorithm and temporal learning strategy that would maximally transfer label data and models from U.S. to foreign countries. The proposed method utilizes adversarial training and contrastive learning. Through this two-player game, the feature extractor produces domain-invariant features. A classifier trained on this domain-invariant representation can transfer its model to a new domain because the target features match those seen during training, thus bridging the gap between times and locations. The U.S. trained algorithm will be tested in Canada and Brazil to demonstrate its cross-country and cross-hemisphere transferability. Scientifically this project will advance landcover science in in-season crop mapping by offering a novel method of transfer learning, advance machine learning in unsupervised domain adaptation across both space and time, and offer new methods to derive spatiotemporally invariant features from time-series remote sensing images. Socioeconomically this project will enhance competitiveness and profitability of U.S. agriculture, increase food security of the world, and potentially bring billions-of-dollars benefits to U.S. farmers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

作物生产是美国（美国）的主要产业。 2021年，美国谷物出口占国际谷物贸易的40％以上。数百万的美国农民依靠国际生活和繁荣。但是，美国谷物出口不仅面临其他出口国的艰难竞争，而且还受到进口国谷物产量的影响。为了获得竞争优势，利益相关者需要尽早了解每种在世界各地种植季节种植的农作物的何处和多少英亩，以便可以评估产量，可以评估产量和需求平衡，并可以预测谷物价格。这就需要在美国和国外产生季节内作物地图。生成季节裁剪地图的经典方法需要大量有关农作物（即地面真相）的验证信息，以训练算法，以分类季节内卫星遥感图像。但是，在国外，特别是在初期，很难甚至不可能获得地面真相。这项研究建议开发一种可随时可转移的机器学习算法，该算法将通过美国数据进行培训，并应用于季节卫星遥感外国国家的遥控图像，以创建国家的季节作物地图。该项目的成功将使国外的季节作物制图成为可能。该项目将大大提高美国农业的竞争力和盈利能力，提高世界的粮食安全，并有可能为美国农民带来数十亿美元的经济利益。卫生馆遥感和地面真相标记是当前的作物制图实践。但是，它遇到了两个问题：1）在国外无法获得地面真理； 2）训练有素的分类器的时空不可替代性。这项研究将设计可随时转移的学习算法和时间学习策略，该策略将最大程度地将标签数据和模型从美国转移到国外。提出的方法利用对抗性训练和对比度学习。通过这个两个玩家游戏，功能提取器可产生域不变功能。在此域不变表示上训练的分类器可以将其模型转移到新域，因为目标特征匹配训练过程中的目标，从而弥合了时间和位置之间的差距。美国训练的算法将在加拿大和巴西进行测试，以证明其越野和跨山脉的转移性。从科学上讲，该项目将通过提供一种新颖的转移学习方法，在无监督的域进行适应的新方法，并提供新的方法，并提供新的方法，并提供新的方法来从时空序列遥感的遥感图像中得出空间不变的特征，从而在季节作物映射中推进陆生科学。从社会经济上讲，该项目将提高美国农业的竞争力和盈利能力，提高世界的粮食安全，并可能为美国农民带来数十亿美元的福利。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来支持的。